Article

A GIS based emissions inventory at 1 km × 1 km spatial resolution for air pollution analysis in Delhi, India

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Abstract

In Delhi, between 2008 and 2011, at seven monitoring stations, the daily average of particulates with diameter <2.5 μm (PM2.5) was 123 ± 87 μg m−3 and particulates with diameter <10 μm (PM10) was 208 ± 137 μg m−3. The bulk of the pollution is due to motorization, power generation, and construction activities. In this paper, we present a multi-pollutant emissions inventory for the National Capital Territory of Delhi, covering the main district and its satellite cities – Gurgaon, Noida, Faridabad, and Ghaziabad. For the base year 2010, we estimate emissions (to the nearest 000's) of 63,000 tons of PM2.5, 114,000 tons of PM10, 37,000 tons of sulfur dioxide, 376,000 tons of nitrogen oxides, 1.42 million tons of carbon monoxide, and 261,000 tons of volatile organic compounds. The inventory is further spatially disaggregated into 80 × 80 grids at 0.01° resolution for each of the contributing sectors, which include vehicle exhaust, road dust re-suspension, domestic cooking and heating, power plants, industries (including brick kilns), diesel generator sets and waste burning. The GIS based spatial inventory coupled with temporal resolution of 1 h, was utilized for chemical transport modeling using the ATMoS dispersion model. The modeled annual average PM2.5 concentrations were 122 ± 10 μg m−3 for South Delhi; 90 ± 20 μg m−3 for Gurgaon and Dwarka; 93 ± 26 μg m−3 for North-West Delhi; 93 ± 23 μg m−3 for North-East Delhi; 42 ± 10 μg m−3 for Greater Noida; 77 ± 11 μg m−3 for Faridabad industrial area. The results have been compared to measured ambient PM pollution to validate the emissions inventory.

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... The pollution sources in the region can be broadly grouped into natural and anthropogenic. The primary sources of anthropogenic emissions for Delhi NCT are fossil fuel burning in industries, power plants and transport sector (Guttikunda and Calori, 2013;Mohan et al., 2012). Previous studies found that vehicular exhaust is the dominant contributor of NO x and CO whereas industries and power plants came out to be the primary source of SO 2 (Guttikunda and Calori, 2013;Sindhwani et al., 2015). ...
... The primary sources of anthropogenic emissions for Delhi NCT are fossil fuel burning in industries, power plants and transport sector (Guttikunda and Calori, 2013;Mohan et al., 2012). Previous studies found that vehicular exhaust is the dominant contributor of NO x and CO whereas industries and power plants came out to be the primary source of SO 2 (Guttikunda and Calori, 2013;Sindhwani et al., 2015). Guttikunda and Calori (2013) concluded that the primary sources of PM10 and PM2.5 were road dust, transportation, and building activities. ...
... Previous studies found that vehicular exhaust is the dominant contributor of NO x and CO whereas industries and power plants came out to be the primary source of SO 2 (Guttikunda and Calori, 2013;Sindhwani et al., 2015). Guttikunda and Calori (2013) concluded that the primary sources of PM10 and PM2.5 were road dust, transportation, and building activities. Apart from anthropogenic emissions, Delhi NCT is also subjected to wind-blown dust from the Thar Desert, situated in the west and south-west region (Ramanathan and Ramana, 2005;Srivastava et al., 2011;Tiwari et al., 2009). ...
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The increased levels of ambient air pollution in Delhi are hazardous to human health and a matter of concern for policymakers. To develop effective regulatory policies, it is necessary to gain a better understanding of the regional background concentrations of pollutants. In the present study, we proposed a method to estimate regional background concentration for the Delhi NCT region. The study uses an integrated approach that employs meteorological filtering and statistical techniques. The meteorological filtering factored in the phenomena of stagnation caused by low wind speed and temperature inversion. Whereas spectral analysis and the Kolmogorov-Zurbenko (KZ) filter were used in combination to reduce the influence of synoptic fluctuation in the time series. Our estimated annual average background concentration for PM10, PM2.5, NO2, SO2 and CO are 56 μg/m³, 42 μg/m³, 18 μg/m³, 7 μg/m³ and 558 μg/m³ respectively. The estimated background concentrations were validated using the ambient pollutant concentration recorded during the 2020 Covid-19 lockdown period.
... On-road vehicles are responsible for both exhaust and nonexhaust emissions (i.e., brake wear, tire wear, road surface wear, and resuspension of road dust). However, most of the available studies have only focused on exhaust emissions (Baidya and Borken-Kleefeld, 2009;Goel and Guttikunda, 2015;Gurjar et al., 2004;Jain et al., 2016;Mohan et al., 2012;Nesamani, 2010;Sadavarte and Venkataraman, 2014) and in some cases specific non-exhaust source, i.e. particulate matter resuspension, brake wear, tire wear, and road wear (Gargava et al., 2014;Guttikunda and Calori, 2013;Guttikunda et al., 2019a;Kumari et al., 2013;Majumdar et al., 2020;Nagpure et al., 2016;Sahu et al., 2011). Currently, studies on contribution analysis of both exhaust and non-exhaust vehicular emissions at high resolution (ward and village scale) for both urban and rural areas of any region of India are unavailable. ...
... The popular method to formulate a high-resolution emission inventory involves estimating emissions using total vehicles registered in an area, vehicle kilometer travel (VKTs), and corresponding emissions factors. The method further distributes estimated emissions into different grids by using distribution indices such as population, road network, and vehicle ownership data (Guttikunda and Calori, 2013;Sahu et al., 2011;Sharma and Dixit, 2015;Sindhwani et al., 2015;V. Singh et al., 2020;Jiang et al., 2020). ...
... A comparison of percentage contribution from exhaust and non-exhaust PM 2.5 emissions to previous studies is presented in table S4. Studies carried out by Sahu et al., 2011, Guttikunda and Calori, 2013, Gargava et al., 2014 estimated emissions for particulate matter resuspension but did not consider any type of wear emissions. Emission estimation study by Nagpure et al., 2016 calculated particulate matter resuspension leaving out emissions from road abrasion, whereas Kumari et al., 2013 only estimated all nonexhaust emissions except particulate matter resuspension. ...
Article
Air quality deterioration due to vehicular emissions in smaller Indian cities and rural areas remains unacknowledged, even though the situation is alarmingly similar to megacities. The resulting lack of knowledge on travel behavior and vehicle characteristics impacts accuracy of emission studies in these regions. This study uses a novel approach and appropriate primary and secondary data sets to allocate vehicular activities (vehicle population and vehicle kilometer travelled) and associated emissions at a high spatial resolution for estimation and dispersion analysis of vehicular exhaust and non-exhaust PM2.5 emission in an Indian urban-rural landscape. The study indicates that using approaches that do not allocate vehicles kilometers travelled to areas of their expected travel results in underestimating the percent share of PM2.5 emissions from rural roads and motorways while overestimating overall PM2.5 emissions. Particulate matter resuspension is the dominant form of PM2.5 emissions from the vehicular sector on all road types, constituting an even higher fraction on rural roads. Two-wheelers contribute a high fraction of PM2.5 emissions (exhaust and non-exhaust combined), followed by heavy commercial vehicles and four-wheelers on urban roads. Light commercial vehicles, especially agricultural tractors dominate these emissions on rural roads. PM2.5 hotspots are prevalent in urban areas, but several rural areas also experience heavy particulate matter concentrations. Thus, vehicle movement incorporation results in more accurate emission estimation, especially in an urban-rural landscape.
... In 2011, Delhi had a population of 16.7 million and, along with its contiguous cities (see Figure 1), forms an agglomeration of 22.5 million, with a population density of 240 person per hectare (Goel and Guttikunda, 2015). In 2014, the annual average PM 2.5 concentration was 146 ug/m 3 (median: 111 ug/m 3 and interquartile range: 66-374 ug/m 3 (Goel, Gani et al., 2015)) and contributed to by multiple sectors (Guttikunda and Calori, 2013). With these concentration levels, Delhi is one of the most polluted cities in the world. ...
... In 2014, PTWs, cars, three-wheeled auto rickshaws and buses contributed 12%, 18%, 1% and 5% of transport PM 2.5 emissions, respectively, and lightand heavy-duty freight vehicles contributed 64% of emissions. We updated the atmospheric transport modelling system (ATMoS) dispersion model for the Greater Delhi region (Guttikunda and Calori, 2013) with spatially and temporally resolved transport emissions. ...
... For the three scenarios, we estimated changes in health burden for the two pathways of injuries and physical activity. We did not conduct scenario modelling of air pollution as there are multiple anthropogenic sources of pollution in Delhi besides transport contributing significantly to overall PM 2.5 concentrations, and there remains large uncertainties in terms of their future trajectories (Guttikunda and Calori, 2013). Because the scenario cities have negligible use of PTWs, the mode share of scenario car trips was distributed among cars and PTWs in the same ratio as in the baseline Delhi (see Table 2). ...
Article
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Transportation impacts population health through air pollution, traffic injuries and physical activity. In the cities of low-and middle-income countries, where travel patterns are rapidly changing, the understanding of these impacts on health is limited. We estimate the health loss among adults (≥15 years) that can be attributed to motorised transportation systems and health benefits attributed to active travel in New Delhi in the year 2014. We show that under baseline transport patterns , health loss is dominated by road traffic injuries (170,000 disability-adjusted life years, DALYs), which is about three times the burden due to traffic-related fine particulate matter (PM 2.5) pollution (~64,000 DALYs). Baseline use of active travel, on the other hand, prevents health burden (~90,000 DALYs), which is as large as 40% of the combined health loss due to injuries and vehicular air pollution. Next, we estimate the effect of changing Delhi's travel modal shares to that of London, New York City and Amsterdam. For these scenarios, we limited to the impact on injuries and physical activity. In all scenarios, there is additional health burden due to traffic injuries and reduced physical activity, and the former exceeds the latter. Greater motorisation in the future is likely to result in large burden of health due to injuries and reduced physical activity. Small reductions in active travel have the potential to negate health benefits from large reductions in traffic emissions. There is an urgent need to develop an alternative pathway of development that is not based on greater use of private motor vehicles.
... Pandey and Venkataraman (2014) estimated that India emitted 4900 Gg of NMVOCs annually from residential cooking. Guttikunda and Calori (2013) estimated the emission of 17 Gg of VOCs from domestic activities over Delhi region for the year 2010. ...
... Crop residue and charcoal burning accounted for 0.04 Gg/yr and 0.005 Gg/yr of NMVOCs respectively. Estimates of the annual emissions of NMVOCs from domestic fuels over Delhi have been reported by quite a few studies (Gurjar et al., 2004;Guttikunda and Calori, 2013;Jain et al., 2014) often with an undefined number of NMVOC species included. Hence, it was difficult to directly compare the variations in total annual NMVOC emissions for different years. ...
... Jain et al. (2014) reported total NMVOC emission from crop residues which include open burning as well as residential usage to be 0.26 Gg/yr for 2009. Guttikunda and Calori (2013) reported total NMVOC emission in the order of 17.3 Gg/yr from residential activities using domestic fuel (kerosene, LPG, solid biofuel) for the year 2010 using a modelling approach. ...
Article
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In controlled laboratory conditions, 62 samples of domestic fuels collected from 56 grids of Delhi were burnt to quantify the emissions of 23 non-methane volatile organic compounds (NMVOCs), i.e., alkanes (11), alkenes (6), alkynes (1) and aromatic compounds (5). The domestic fuels used for residential activities were comprised of 20 unique types of fuel woods, 3 species of crop residue, dung cakes and coal. These fuels are primarily used for cooking and water/space heating during winters. The current study reports the total emission budget of NMVOCs from domestic burning over Delhi. Furthermore, this study also compares the differences in EFs of NMVOCs which are calculated for different burning cycles and sample collection methods. The EFs of NMVOCs calculated from the samples collected during the flaming stage using canisters were analysed for 23 NMVOCs and then compared with same species emitted from complete burning cycle. In addition to this, 10 consumption and emission hotspot grids were also identified in Delhi; based on the ground survey and laboratory simulated results. The total annual usage of domestic fuels for the year 2019 was found to be 0.415 Mt/yr (million tonnes) in Delhi. 12.01 Gg/yr of annual NMVOC emissions was calculated from domestic fuel burning in which the emissions from dung cake and fuel wood dominated with 6.6 Gg/yr and 5.4 Gg/yr, respectively. The EFs of NMVOCs calculated using canister and online collection method differ significantly from each other. The flaming stage presented enhanced emissions compared to the complete burning cycle by ∼7 times which suggests that the method of data analysis and the period of sample collection play a pivotal role in the preparation of an emission inventory and estimating the budget.
... There were various attempts to estimate the emissions from these sources. Guttikunda and Calori (2013) made emission inventory for particulate matter and various trace species at a resolution of 1 km × 1 km ( Table 3). The major pollution sources are transportation, power generation, and construction activities. ...
... All these three sources were almost closed during the lockdown. Similarly, major sources of VOCs include transportation (∼50%), brick kiln, and power plants (Guttikunda and Calori, 2013). The first two were totally closed during the lockdown. ...
Article
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The COVID-19 pandemic resulted in changed emission regimes all over the world. India also imposed complete lockdown on all modes of travel and industrial activities for about 2 months from 25-March-2020 and later unlocked these activities in a phased manner. Here, we study signatures of emissions changes on levels of atmospheric trace gases and aerosols contributing to air pollution over multiple sites in India’s capital Delhi covering various lockdown and unlock phases using satellite data and in-situ observations. The resulting changes in the levels of these species were compared with respect to their average of 2015–2019 to attribute for year to year and seasonal changes. A clear impact of lockdown was observed for AOD, PM, NO2, CO, and SO2 as a result of emission changes, while changed precursor levels led to a change in O3 chemical regimes impacting its concentrations. A detailed analysis of FLEXPART trajectories revealed increased PM levels over Delhi in north-westerly air masses sourced to Punjab region all the way up to Pakistan. Changes in aerosols and NO2 were not only restricted to the surface but transcended the total tropospheric column. The maximum decrease in PM, NO2, CO, and SO2 was observed during the month of total lockdown in April. The lockdown impact varied with species e.g., PM10 and PM2.5 as well as locations even within the periphery of Delhi. While surface level aerosols and NO2 showed significant and almost similar changes, AOD showed much lower decrease than tropospheric column NO2.
... for PM 10 and 2.3-28.9% for PM 2.5 (Indian Institute of Kanpur, 2016;Guttikunda and Calori, 2013;TERI Report, 2018., Sahu et al., 2011, thereby affecting proper management and proper policy intervention for the city. ...
... For this, source apportionment studies are done by collecting a large number of ambient samples from diverse settings (road, industrial, residential, and background), analyzed for their chemical profiles, and then statistically matched with a set of known source profiles, and to establish the source shares. In Delhi, some such source apportionment studies indicate vehicular pollution as prime culprit for Delhi's deteriorating air quality, while others have identified non exhaust sources as key contributors in hazardous levels of air pollutants in the city (CPCB, 2010; Indian Institute of Kanpur, 2016; Guttikunda and Calori, 2013). Considering the high sensitivity of the biomagnetic technique, the present study was carried out in the highly polluted metropolitan city of Delhi for the first time to monitor and characterize the atmospheric particulate matter in some high air pollution areas based on magnetic characteristics of dust laden roadside tree leaves. ...
Article
With increasing atmospheric pollution and health issues associated with size of the particulate matter, it has become important to look for techniques that may improve the monitoring resolution. Magnetic bio-monitoring of particulate matter has been used in recent years in some countries as an approach for better spatial resolution that provides proxy indicators for the measurements over large areas. Delhi, which is one of the most polluted cities of not just India, but the whole world, is still probing to understand the possible sources. The present magnetic biomonitoring study was therefore, carried across different land use areas in some air pollution hotspots of Delhi, using common roadside tree species Morus alba, Ficus religiosa, Ficus variens and Ficus benghalensis to understand the magnitude and nature of the particulate pollution, and possible sources by studying magnetic properties (Magnetic susceptibility, Frequency-dependent susceptibility, S-ratio, and SIRM) of the dust deposited on leaves. Mass specific magnetic susceptibility (10⁻⁸ m³ kg⁻¹) values found to follow the order: Traffic intersection area (25.6–66.5) > Industrial area (25.4–41.3) > Residential area (13.2–30.1) > Institutional area serving as control (2.7–6.6). High magnetic susceptibility values indicated particulates with ferrimagnetic grains of anthropogenic or technogenic origin. Frequency-dependent Susceptibility indicated dominance of coarse multidomain (MD) and Pseudo Single Domain (PSD) +MD grains in industrial area and major traffic intersection. Average S ratio across all study sites ranged from 0.92 to 0.99 indicating presence of soft magnetic mineral with low coercivity. High SIRM values (10⁻⁵Am² kg⁻¹) from 58.1 to 862.3 suggested prevalence of magnetite dominating atmospheric particulates particularly in traffic intersection and industrial area, and to some extent in residential area. Morus alba and Ficus religiosa were found more suitable bio-monitors and the technique provided useful information on size, mineralogy and possible source of the particulates.
... Number of programs have LCS based operational services (Supplementary Material: Section I). However, data accuracy Kaivonen and Ngai, 2020;Kumar et al., 2015;Maag et al., 2018;Xie et al., 2017;Jerrett et al., 2005;Karagulian et al., 2019;Zheng et al., 2019b;Hagan et al., 2019;Heimann et al., 2015;Van den Bossche et al., 2015;Apte et al., 2017;Caubel et al., 2019;Castell et al., 2018;Engel-Cox et al., 2013;Reis et al., 2015;Schneider, 2017;Arano et al., 2019;Zalakeviciute et al., 2018;Steinle et al., 2015 ;Alexeeff et al., 2018;Dutta et al., 2016;Broday and Represa et al., 2020;Zheng et al., 2014a;Svetnik et al., 2003;Belavadi et al., 2020;Gryech et al., 2020;Mahajan and Kumar, 2020;Zheng et al., 2015;Ly et al., 2019;Yu et al., 2016;Bai et al., 2018;Lu et al., 2021;Feng et al., 2017;Hu et al., 2014;Maag et al., 2018;Jiang et al., 2015;Zheng et al., 2019aZheng et al., , 2019bAmeer et al., 2019;Athira et al., 2018GSMA, 2018cKök et al., 2017;Reddy et al., 2017;Zhou et al., 2018;Zheng et al., 2020;Yazdi et al., 2020;Agarwal et al., 2020;Fazziki et al., 2015;Kumar and Goyal, 2013;Pan et al., 2017;Relvas and Miranda, 2018;Habibzadeh et al., 2019;Liao et al., 2020 Big data (Section 4.6) * * * * Tomlinson, 1968;Li et al., 2020;Gorai et al., 2018;Ramos et al., 2018;Behera et al., 2015;van Zoest et al., 2020: Badach et al., 2020Sun et al., 2020;Dong et al., 2020;Behera et al., 2011;Dalvi et al., 2006;Guttikunda and Calori, 2013 and all-weather maintenance, sensor life expectancy are major challenges for its widespread real time applications. For instance, by applying ML based correlation correction method for temperature and humidity, researchers (Johnson K et al., 2020), have demonstrated 90 % of the time, an improvement in the nowcast Air Quality Index (AQI) using PurpleAir sensors. ...
... In one study, Dong et al. (2020) could generate spatial patterns at 30 m resolution SO 2 population exposure from industry and vehicle sources by integrating dispersion models, GIS, population dynamics, and spatial interpolation techniques. GIS has also been found to be useful in preparing EI with micro-scale mapping of emission load data with city grids (Behera et al., 2011;Dalvi et al., 2006;Guttikunda and Calori, 2013;Singh Dhirendra et al., 2016). ...
Article
Cities foster economic growth. However, growing cities also contribute to air pollution and climate change. The paper provides a perspective regarding the opportunity available in addressing the urban air quality management (UAQM) issues using smart city framework in the context of ‘urban computing’. Traditionally, UAQM has been built on sparse regulatory monitoring, enhanced with satellite data and forecast models. The ‘Fourth Industrial Revolution’ (4IR) technologies such as Internet of Things (IoT), big data, artificial intelligence, smartphones, social and cloud computing are reshaping urban conglomerates, worldwide. Cities can harness these ubiquitous technologies in concert with traditional methods for betterment of air quality governance and to improve quality of life. This paper discusses the role of urban computing in UAQM through a review of scientific publications and ‘grey literature’ from technical reports of governments, international organizations and institutional websites. It provides an interdisciplinary knowledge repository on urban computing applications for air quality functions. It highlights the potential of integrated technologies in enabling data driven, strategic and real-time mitigation governance actions and helping citizens to take informed decisions. It recommends ‘fit for the purpose’ multitechnology framework for UAQM services in emerging smart cities.
... For instance, open burning emitted 2.470 Gg/year of CO or 30-fold lower emission than that estimated in Ibadan City, Nigeria [25]. In addition, another researcher estimated that the PM 2.5 emission in Semarang City was 1.5-fold higher than that in the Delhi municipalities [26]. Therefore, it is estimated that the emissions from Semarang City are higher than those reported in other studies that used the same transect walk methods [15]. ...
Article
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In this study, total burned household waste and the potential emissions released from waste burning in Semarang City, Indonesia, were estimated. Waste piles were monitored using the transect walk survey method in 16 sub-districts of Semarang City. Carbon monoxide (CO), carbon dioxide (CO2), hydrocarbon (HC), nitrous oxide (NOx), and total particulate matter (TPM) were directly analyzed through a simulation of waste combustion. The potential emissions from other pollutants were predicted by multiplying the weight of the burned waste by the emission factors available in the literature. The estimated waste burned in Semarang City in 2020-2021 was 58.8 Gg/year, or approximately 9.70% of the total waste generated in Semarang City. This estimation exceeds local government estimates of 2020 by two-fold. Peri-urban areas (both inner and outer) were identified as the most significant contributors to waste burning. Further, garden waste was the most burned waste (73.61%), followed by plastic waste (17.45%). Other wastes, including paper, leather, textile, rubber, and food, were also burned. Overall, a decrease in the activity of waste burning is an important step for reducing the potential of air pollution and climate change. Supplementary information: The online version contains supplementary material available at 10.1007/s10163-022-01371-3.
... The primary sources of air pollution in cities of India are vehicular emissions, industrial emissions, coal combustion, biomass burning, road dust and waste burning, construction activities, oil combustion etc. 28 There exists a strong linkage between air pollution and adverse health impacts. The total Disability Adjusted Life Years (DALYs) and the total number of deaths attributable to outdoor air pollution in India in 2012 were 20506015 and 621137 respectively 73 . ...
Article
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On March 16, 2020, Kolkata megacity in India announced partial lockdown due to COVID-19 crisis. This study presents an analysis for multiple pollutants with special focus on NO2 and O3 based on data from different monitoring stations located to across Kolkata city for the period from 16 March-17 May 2020 compared to the pre-lockdown period. Most significant reduction was observed in the concentration of nitrogen dioxide (NO2) (-76.8%), volatile organic compounds (VOCs) (-69.5%), PM10 (-64.6%) and PM2.5 (-60.9%). A lower reduction percentage was found for CO, sulfur dioxide (SO2) and ammonia (-48.6%,-41.7% and-41.1% respectively). However, during partial lockdown, lockdown phase-1, phase-2 and phase-3, surface-level ozone (O3) has changed respectively by 31.72%, 31.13%,-14.28% and-14.05% which resulted in an overall increase of 8.17% in the entire study period. The air quality index (AQI) in Kolkata which usually remains poor or very poor, even during lockdown period, failed to attain the 'good' standard. This needs special attention in human health impact assessment and public health management. We recommend additional attention to be drawn towards stickiness in O3 which had adverse human health and which went up during lockdown period compared to pre-lockdown period. We highlight some major policy implications of the observed trends to combat city air pollution along with climate co-benefits by shifting transport fuel and related infrastructure.
... Increased economic vulnerabilities and reduced access to nutrition is linked to morbidity and mortality (Rao et al., 2009). Second, environmental exposures, such as pollution, heat, humidity, and cold weather have direct impacts on health and they vary seasonally (Geruso and Spears, 2018a;Guttikunda and Calori, 2013;Kumar et al., 2009). Third, disease dynamics, particularly for communicable diseases, are driven by individual behaviors, environmental effects on hosts and pathogens, and their interactions (Fisman, 2007). ...
Article
This dissertation makes three scientific contributions to understand the ongoing epidemiological transition in India. The first chapter documents local externalities of solid fuel use for adult lung function. The use of solid fuels for cooking and heating is rooted in poverty and gender inequality within households. However, harms from solid fuel use are more widespread. In neighborhoods with high solid fuel use, the lungs of those who do not use solid fuels can be as obstructed as the lungs of those who use solid fuels. Because it contributes to both infectious disease among children and chronic diseases among adults, the use of solid fuels complicates the epidemiological transition in India. The second chapter observes that Indian infants face higher mortality risks in the summer, monsoon, and winter months compared to the spring months. Using birth history data, the chapter develops an innovative demographic approach which estimates and adjusts infant mortality by calendar month. The chapter highlights that Indian infants face multiple environmental threats that are less salient for a limited period within a year. It finds that although seasonal variation has declined, it remains a concern in rural areas and among more disadvantaged households. The last chapter provides the first estimates of life expectancy by social class in India for the period 1990-2016. It develops methods to directly estimate life tables from survey data. The chapter documents persistent and stark mortality disparities in a period of robust economic growth and changing disease profiles. It finds progress in reducing levels and differentials in child mortality. However, patterns in the working ages are concerning, with slower progress and little reduction in inequality. The three chapters make both substantive and methodological contributions to the study of health and mortality in low- and middle-income countries. They show that addressing social inequalities and environmental risks are essential for population health improvements in India.
... A city's socioeconomic information such as projected population growth, residential area, business hubs, recreation areas, and industry location contribute to the anthropogenic emission source identification (Guttikunda et al., 2013). Geo-referenced ward-wise shapefiles and point shapefiles are part of the city's geospatial database. ...
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Rapid urbanization across the world has put an enormous burden on our environment. Cities from developing countries, in particular, are experiencing high air pollution levels. To address this challenge, the new WHO global air quality guidelines and various nations are mandating cities to implement clean air measures. However, these implementations are largely hindered by limited observations, siloed city operations, absence of standard processes, inadequate outreach, and absence of collaborative urban air quality management (UAQM) governance. The world is experiencing transformative changes in the way we live. The 4th industrial revolution technologies of artificial intelligence, Internet of Things, big data, and cloud computing bridge gaps between physical, natural, and personal entities. Globally, smart cities are being promulgated on the premise that technologies and data aid in improving urban services. However, in many instances, the smart city programs and UAQM services may not be aligned, thereby constraining the cumulative advantage in building urban resilience. Considering the potential of these technologies as enablers of environmental sustainability, a conceptual urban computing framework “SmartAirQ” for UAQM is designed. This interdisciplinary study outlines the SmartAirQ components: 1) data acquisition, 2) communication and aggregation, 3) data processing and management, 4) intelligence, 5) application service, 6) high-performance computing- (HPC-) cloud, and 7) security. The framework has integrated science cloud and urban services aiding in translating scientific data into operations. It is a step toward collaborative, data-driven, and sustainable smart cities.
... Ground observations of SO 2 are generally low across the country with high concentrations found at a few urban and industrial locations. This has been corroborated by previous studies (Guttikunda and Calori, 2013). The role of alkaline dust in scavenging SO 2 in India likely reduces ambient concentrations (Kulshrestha et al., 2003). ...
... Delhi: As Delhi is the capital of India and one of the most polluted cities in the world, many studies have been conducted to estimate the emission load share of different polluting sectors (Guttikunda & Calori, 2013;Mishra & Goyal, 2015;M. Sharma & Dikshit, 2016;Sindhwani et al., 2015;TERI & ARAI, 2018a). ...
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... While the HK sampling site was located inside an educational institute campus (IITD) in south Delhi, which is also surrounded by educational institutions, residential and market areas along with a major traffic emissions source (outer ring road; ∼100 m from the sampling site). Both sampling sites are representative of urban emissions in Delhi which ranges from power plants (2 coal and 4 natural gas-based), medium and small scale industries, brick kilns, vehicles (11 million in 2018), domestic cooking and seasonal agricultural waste burning from neighboring states of Punjab and Haryana in April-May and October-November months (Guttikunda & Calori, 2013;Lodhi et al., 2013;Rai et al., 2020;Sharma et al., 2019). Supplementary data (PM 2.5 mass, SO 2 , ozone [O 3 ]) and solar radiation (SR) were obtained from nearby air quality monitoring stations viz. ...
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Delhi metropolitan area suffers from extreme haze during the post-monsoon and winter season, impacting climate, public health, and economy. We used a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) and aethalometer at two urban locations in Delhi to capture non-refractory PM2.5 (NR-PM2.5) and black carbon (BC) during the post-monsoon and winter season of 2019-20. Four haze periods with high composition based-PM2.5 (C-PM2.5=NR-PM2.5+BC) concentration and distinct chemical composition were identified, during all of which organics dominated but with varying contribution (∼(50-70)% of C-PM2.5). Biomass burning organic aerosol (BBOA) was dominant in all periods (∼(31-45)% of OA), but the majority of it was highly aged ∼(45-50)% with high O/C (0.71 and 0.46 at the two sites), formed most likely through rapid dark oxidation of freshly emitted and partially oxidized BBOA. High polycyclic aromatic hydrocarbons (PAH) signals in the fresh BBOA mass spectra suggest incomplete combustion activities such as open biomass burning emissions as major source. During an agricultural burning event in north-western India, we estimated that ∼(44-53)% of total C-PM2.5 (combined contribution of aged BBOA and oxygenated OA) measured in Delhi was influenced by long-range transported biomass burning emissions. During winter, secondary inorganics constituted a significant fraction apart from organics ∼(48-55)%, mainly in the form of ammonium nitrate (NH4NO3; up to ∼(19-25)% of C-PM2.5) and ammonium sulfate (NH4SO4; up to ∼(27-38)%). Enhanced formation of NH4NO3 and related-secondary organic aerosol (SOA) were linked to nighttime oxidation of BBOA, while NH4SO4 and related-SOA were linked to heterogeneous aqueous phase oxidation under high RH conditions (>90%).
... In common with China, India (Lu et al., 2011;Lu and Streets, 2012), Japan (Kannari et al., 2007), and Korea (Lee et al., 2011) have also constructed high-resolution gridded national emission inventories to be utilized in regional and global models. Besides, some small-scale air quality studies have been developed for Asian megacities, like Delhi (Guttikunda and Calori, 2013), Istanbul (Markakis et al., 2012), Seoul (Jung and Kwon, 2015), and Beijing (Hao and Wang, 2005). These detailed researches are conducted generally utilizing the "bottom-up" approach integrated with locally modified emission factors (Zhou et al., 2014). ...
Article
Many highly populated cities are still struggling to reach clean air targets, while the zero greenhouse gas emission objectives may accelerate the path toward healthy air for all. Still, there is a fine line between intensive electrification's impact on greenhouse gas emissions and criteria air contaminants depending on the source of the electricity. In this study, the previous version of the emission inventory for Tehran was evaluated and re-calculated in a detailed bottom-up approach to provide the most updated data on the contribution of stationary sources derived by power plants vs. mobile sources. The objectives were to update the emission inventory for improved policymaking, study the impact of changes in emissions in 4 years, provide a detailed methodology for cities highly impacted by transportation emissions, and exercise a simple yet effective modelling task for emission inventory evaluation. The study included all possible sources in the mobile sector with eight categories of fleet composition, industries, power plants, house heating, and point sources related to transportation such as terminals and gas stations. It also included exhaust, non-exhaust, and evaporative emissions for mobile sources. An intensive data collection campaign was launched to collect activity data. The traffic information was obtained from a travel-demand model and validated by traffic counting. The license plate registration database provided detailed fleet composition based on vehicle technology, fuel, age, and state of maintenance. The emission inventory calculated annual emissions of 478 kt for CO, 103 kt for NOx, 91 kt for VOCs, and 19 kt for SOx. TSP was estimated at 10.4 kt. NOx emissions were significantly increased from 87 to 102.6 ktonnes per year (17% increase) and were confirmed by an increase in the annual mean concentration of NO2 from 39.4 to 53.4 ppb (35% increase) between 2013 and 2017. A notable observation was the impact of house heating on CO2,eq emissions. More than 30% of CO2,eq emissions were from house heating (natural gas, predominantly methane) in a city where total air contaminant emissions are highly driven by the transportation sector. The result clearly shows the differences between zero-emission target paths for the two objectives of greenhouse gas emissions and air quality. An integrated approach is needed to develop policies that will lead o zero-emission GHG targets while keeping the air clean.
... This study agrees that vehicular emissions of NMVOCs are dominant in the city. Although, other source factors identified, did not compared well to the present study and other ambient measurement studies (Wang et al., 2020a) and emission inventories (Guttikunda and Calori, 2013;Sharma and Khare, 2017). This could be due two main reasons; firstly, the data was collected during shorter measurement periods and principal component analysis (PCA) and Unmix models were used instead of more robust PMF model. ...
Article
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Volatile organic compounds (VOCs) are ubiquitous atmospheric constituents and play important roles in tropospheric photochemistry. The real-time chemical characterization of VOCs was carried out in this study using the proton-transfer-reaction time-of-flight mass spectrometer (PTR-TOF-MS) at an urban site of Delhi for the complete year of 2019. The average concentrations of total VOCs ranging from m/z 31.02 to 181.12 were 131.3 ± 96, 78.7 ± 47.3, 151.2 ± 83.2, 199.6 ± 101.2 ppbv in the winter, summer, monsoon, and post-monsoon seasons, respectively. The positive matrix factorization (PMF) receptor model on an advanced ME-2 engine was used to perform source apportionment analysis. This analysis reveals two traffic-related factors, two solid-fuel combustion factors, two secondary VOC factors, one biogenic, and one solvent-use factor depending upon the season. The traffic-related emissions mainly comprised of aromatics and simple non-aromatics, contributed about 31% (∼one-third) of the total VOCs measured over the study period. Solid fuel combustion factors consisting of phenols, furans, and nitrogen-containing compounds contributed about 28%, while Secondary VOCs contributed about 31% cumulatively over the study period. The contributions of biogenic sources were significant only during the summer and monsoon seasons. A local source near the sampling site was identified as a solvent-use factor, mainly comprised of ethyl acetate due to the renovation work near the sampling site during monsoon and post-monsoon season. The difference in the profile of primary sources was influenced by the local emissions and regional transport of air masses. Meteorological conditions and planetary boundary layer highly influence the formation of secondary VOCs.
... Such heavy consumption of coal makes this sector an important contributor to GHGs. Moreover, the industry is still reliant on traditional and inefficient technologies, which emit particulate matter along with CO and SO2 and thereby degrade the air quality around major urban centres of India (Guttikunda & Calori, 2013;Kumbhar et al., 2014). Brick production in India largely employs four major technologies: clamps, fixed chimney Bull's trench kilns (FCBTKs), zigzag-fired kilns and vertical shaft brick kilns (VSBKs). ...
... The built-up area of Delhi has expanded steadily from 195.30 km 2 in 1989 to 435.12 km 2 in 2020 [29,30]. This urban growth has led to environmental degradation, particularly in the last few years [31][32][33]. Many studies have discussed the land use/cover change in Delhi and its impacts [34][35][36]. ...
Article
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During the last three decades, Delhi has witnessed extensive and rapid urban expansion in all directions, especially in the East South East zone. The total built-up area has risen dramatically, from 195.3 sq. km to 435.1 sq. km, during 1989-2020, which has led to habitat fragmentation, deforestation , and difficulties in running urban utility services effectively in the new extensions. This research aimed to simulate urban expansion in Delhi based on various driving factors using a logistic regression model. The recent urban expansion of Delhi was mapped using LANDSAT images of 1989, 2000, 2010, and 2020. The urban expansion was analyzed using concentric rings to show the urban expansion intensity in each direction. Nine driving factors were analyzed to detect the influence of each factor on the urban expansion process. The results revealed that the proximity to urban areas, proximity to main roads, and proximity to medical facilities were the most significant factors in Delhi during 1989-2020, where they had the highest regression coefficients: −0.884, −0.475, and −0.377, respectively. In addition, the predicted pattern of urban expansion was chaotic, scattered, and dense on the peripheries. This pattern of urban expansion might lead to further losses of natural resources. The relative operating characteristic method was utilized to assess the accuracy of the simulation, and the resulting value of 0.96 proved the validity of the simulation. The results of this research will aid local authorities in recognizing the patterns of future expansion, thus facilitating the implementation of effective policies to achieve sustainable urban development in Delhi.
... About 1000 brick kilns or more in Delhi's vicinity are supposed to be significant contributors (10%) of total air pollution by the report [69] in the Delhi-National Capital Region (NCR) region. Guttikunda and Calori [70] over 1,00,000 tons of yearly black carbon emissions are estimated in Indian bricks kilns. The three major air pollutants of brick kilns are dust, sulfur dioxide (SO 2 ), Black carbon, PM 2.5, and nitrogen oxides (NO x ) as per China's National Mandatory Standard [71]. ...
Article
The extraction of unsustainable natural resources like sand and topsoil for construction is disturbing ecological balance, affecting local hydrology and wildlife. The inter-governmental panel of climate change (IPCC) has discussed its adverse impacts worldwide and has restricted its extraction. This article has mooted some of its potential threats and remedies to combat climate change and other social issues associated with the brick industry. Bricks made of clay are commonly used as a building material for masonry works in densely populated South Asian countries such as India, Pakistan, and Bangladesh. To make clay bricks, the topsoil of the floodplain area where most agriculture productivity is carried out is utilized in millions of tons every year. The main drawbacks of topsoil removal are the depletion of the seed bank, removing soil biota, and diminishing soil properties and functions. Furthermore, the process often involves burning firewood as emitting fuel CO2, SO2, NO2, and suspended particulate matter (SPM). Thus, the clay brick-making industry contributes to greenhouse gases directly affecting soil fertility. The primary focus of this review article is to promote research on flyash-based bricks for the brick industry and provide safe and cost-effective sustainable materials to substitute clay bricks. This review article presents the worldwide production of clay bricks and their harmful impact on the environment. The current and future scenarios on flyash, systematic literature review (SLR) on flyash-based bricks, the practical utility of clay and flyash-based bricks, and issues and opportunities were also discussed. The findings obtained from the past published studies on flyash-based bricks give an insight to the researchers. The review article also presents the prospects for researchers and designers.
... Smaller contributions to the overall VOC burden were found from solid fuel combustion, 16 % (Stewart et al., 2021a) and 27 % . These data were in line with Guttikunda and Calori (2013), who produced an inventory which showed that petrol and diesel sources were responsible for 65 % by mass of hydrocarbons in Delhi. VOC emissions have also been shown to be dominated by petrol and diesel sources (∼ 50 %) in a study which conducted positive matrix factorisation (PMF) analysis on proton transfer reaction mass spectrometer flux measurements taken in a follow-on campaign in early November 2018 at the Indira Gandhi Delhi Technical University for Women (IGDTUW) (Cash et al., 2021). ...
Article
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The Indian megacity of Delhi suffers from some of the poorest air quality in the world. While ambient NO2 and particulate matter (PM) concentrations have received considerable attention in the city, high ground-level ozone (O3) concentrations are an often overlooked component of pollution. O3 can lead to significant ecosystem damage and agricultural crop losses, and adversely affect human health. During October 2018, concentrations of speciated non-methane hydrocarbon volatile organic compounds (C2–C13), oxygenated volatile organic compounds (o-VOCs), NO, NO2, HONO, CO, SO2, O3, and photolysis rates, were continuously measured at an urban site in Old Delhi. These observations were used to constrain a detailed chemical box model utilising the Master Chemical Mechanism v3.3.1. VOCs and NOx (NO + NO2) were varied in the model to test their impact on local O3 production rates, P(O3), which revealed a VOC-limited chemical regime. When only NOx concentrations were reduced, a significant increase in P(O3) was observed; thus, VOC co-reduction approaches must also be considered in pollution abatement strategies. Of the VOCs examined in this work, mean morning P(O3) rates were most sensitive to monoaromatic compounds, followed by monoterpenes and alkenes, where halving their concentrations in the model led to a 15.6 %, 13.1 %, and 12.9 % reduction in P(O3), respectively. P(O3) was not sensitive to direct changes in aerosol surface area but was very sensitive to changes in photolysis rates, which may be influenced by future changes in PM concentrations. VOC and NOx concentrations were divided into emission source sectors, as described by the Emissions Database for Global Atmospheric Research (EDGAR) v5.0 Global Air Pollutant Emissions and EDGAR v4.3.2_VOC_spec inventories, allowing for the impact of individual emission sources on P(O3) to be investigated. Reducing road transport emissions only, a common strategy in air pollution abatement strategies worldwide, was found to increase P(O3), even when the source was removed in its entirety. Effective reduction in P(O3) was achieved by reducing road transport along with emissions from combustion for manufacturing and process emissions. Modelled P(O3) reduced by ∼ 20 ppb h−1 when these combined sources were halved. This study highlights the importance of reducing VOCs in parallel with NOx and PM in future pollution abatement strategies in Delhi.
... Tourism and small particulate matter; Small Particulate Matter refers to solid or liquid particles suspended in the air that are smaller than 10 µm [21]. They originate from natural and human activities such as wildfires, the combustion of fuel in transportation systems, power generation, agriculture, etc. [22]. Studies on the impact of the particulate matter problem are plentiful, especially in countries with this problem happens frequently. ...
Article
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In northern Thailand, the problem of small particulate matter arises every year, with the primary source being agricultural-weed burning and wildfire. The tourism industry is strongly impacted and has been in the spotlight for the past few years. Thus, this study aims to investigate the effect of small particulate matter on tourism and related SMEs in Chiang Mai, Thailand. The data were collected from 286 entrepreneurs in the tourism and related SMEs sectors. The data were analyzed using data mining and association-rule techniques. The study revealed that small particulate matter has a considerable impact on customer factors, especially when the number of customers has decreased. Operational factors and product/service factors are also affected by the dust in the form of adjustments to keep the business running and the protection of the health of employees and customers. Certainly, financial factors are affected by the small particulate matter situation, both lower revenues and higher costs.
... The air quality of Delhi city, which is the capital of India has been degraded so much that even the traffic intervention policies were also failed to moderate the high emission levels (Rizwan et al. 2013;Chowdhury et al. 2017). Various source apportionment studies have identified that vehicles are the major emission source of air pollution in ambient air of Delhi (Srivastava et al. 2005(Srivastava et al. , 2008; Guttikunda and Calori 2013). It is a well-known fact as observed from various epidemiological studies that the exposure to particulate matter (PM) is linked with significant increase in risks of cardiopulmonary illness and lung cancer diseases, resulted in mortality and morbidity worldwide (Brunkreef and Holgate 2002;Sanhueza et al. 2009;Evans et al. 2013;. ...
Article
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The present study was conducted to characterize and assess the magnitude and severity of particulate matter bound B[a]P in outdoor air of Delhi and its potential risks on humans health. For this purpose, the sampling of B[a]P was carried out at six different locations in Delhi for summer and winter seasons. The ambient air samples were collected on Whatman glass fibre filter. The characterization and quantification of PM10 bound B[a]P was carried out in the laboratory through Gas Chromatography. It was found that the levels of B[a]P were much higher in winter (0.54–13.42 ng/m³) as compared to summer (0.08–2.21 ng/m³). Average B[a]P level was found higher than the permissible limit of 1 ng/m³. Statistical analysis suggests that meteorological parameters plays a significant role in B[a]P accumulation in ambient air. The size of particulate matter also affects the B[a]P bounding affinity. The finer particles have shown greater tendency to accumulate organic pollutants on their surface. The Incremental Lifetime Cancer Risk (ILTCR) was estimated by adopting USEPA methodology. It was found that during winter the estimated ILTCR were higher than permissible limit of 1.0E-06 at all locations except an institutional area. The risk vulnerability of different populations groups indicated significant difference in the potential risks. Roadside vendors/workers are more prone to B[a]P associated health risks in comparison of daily commuters mainly due to more exposure time in unfavorable and toxic environmental condition. The ILTCR was found to be almost two times higher for children (1.24E-07–2.07E-05) than for adults (6.42E-08–1.08E-05). Graphic Abstract
... In India, premature deaths due to PM 2.5 exposure have increased significantly since 1990 (as per a recent estimate (2016), approximately 1.0 million premature deaths can be attributed to PM 2.5 exposure (https://vizhub.healthdata.org/gbdcompare/india). Most of the polluted cities of India are concentrated in and around the National Capital Territory of Delhi (NCTD), making this region a leading hub of pollution in the world (Guttikunda and Calori, 2013;Tiwari et al., 2012;Pandey et al., 2016). The cities of NCTD including Delhi, Gurugram, Faridabad (Haryana) and several other cities located in Uttar Pradesh and Bihar are the most vulnerable to PM 2.5 exposures. ...
Article
Present study aims to examine the impact of lockdown on spatio-temporal concentration of PM2.5 and PM10 - categorized and recorded based on its levels during pre-lockdown, lockdown and unlock phases while noting the relationship of these levels with meteorological parameters (temperature, wind speed, relative humidity, rainfall, pressure, sun hour and cloud cover) in Delhi. To aid the study, a comparison was made with the last two years (2018 to 2019), covering the same periods of pre-lockdown, lockdown and unlock phases of 2020. Correlation analysis, linear regression (LR) was used to examine the impact of meteorological parameters on particulate matter (PM) concentrations in Delhi, India. The findings showed that (i) substantial decline of PM concentration in Delhi during lockdown period, (ii) there were substantial seasonal variation of particulate matter concentration in city and (iii) meteorological parameters have close associations with PM concentrations. The findings will help planners and policy makers to understand the impact of air pollutants and meteorological parameters on infectious disease and to adopt effective strategies for future.
... The high diurnal variation of BC can be explained by the local BC sources, including the major ring road near the sampling site with heavy load vehicles (trucks). These trucks are often restricted to only passing through Delhi at night (Guttikunda and Calori, 2013). The BC concentration is five times lower than that reported in winter at the same site by Lalchandani et al. (2021). ...
Article
Delhi is one of the most polluted cities globally, with frequent severe air pollution episodes and haze events occurring in recent years, thereby compelling us to understand the sources to develop effective mitigation plans. Complete chemical characterization of fine particulate matter (PM2.5) components (non-refractory, refractory and elements) with high time resolution has been done during the summer season (June-July 2019). The total PM equivalent (PM2.5(eq)) was 28.7 ± 13.2 μg.m⁻³ of which elements dominated the PM2.5(eq) with 34% contribution followed by organics (28%), black carbon (BC) (17%), SO4²⁻ (10%), Cl⁻ (5%) NH4⁺ (3.5%) and NO3⁻ (2.5%). The contributions from organic aerosols (OA) and SO4²⁻ were observed to be more than Cl⁻ and NO3⁻. The total elemental mass concentration (PMEl) was mostly contributed (∼96%) by Si, S, Cl, Ca, K, Fe and Al with Si and S alone contributing around 50% of PMEl. Crustal elements (Al, Fe, Ca and Si) were highly enhanced in summer than elements emitted from anthropogenic emissions (Cl, S, K, Pb and Zn). Source apportionment (SA) of PM was performed using positive matrix factorization (PMF) together with ME-2 (multilinear engine) for OA and elements, separately. PMF on both datasets helped resolve sources such as combustion, industrial, dust-related, incineration and traffic. OA PMF identified three factors related to primary emissions: hydrocarbon-like OA (HOA, 12.3%), solid fuel combustion (SFC, 16.2%) and cooking OA (COA, 7.3%) and two oxygenated OA (OOA): semi-volatile OOA (SVOOA, 15.2%) and low-volatile OOA (LVOOA, 49.1%). The elemental PMF resolved 8 factors: dust (52.5%), S-rich (16.2%), Cl-rich (10.7%), 2 SFC factors (10.5%), non-exhaust (7.2%), Cu-rich (1.5%) and industrial (1.4%). The contribution of BC to total PM mass is shown to increase in the summer compared to previous studies reported for the winter season. The secondary oxidized sources dominated both the OA and elements SA during the summer with 64.3% and 27% (dust not considered) contribution, respectively. The domination of secondary sources implies that it is crucial to control the secondary aerosols' precursors in Delhi for developing pollution control strategies. The ME-2 resolved factors, coupled with concentration weighted trajectory (CWT) showed the probable major elemental source regions of local origin (Delhi- National Capital Region (Delhi-NCR)) as well as regional (from Punjab, Haryana, Uttar Pradesh and Pakistan). The local sources included Cu-rich (Haryana) and SFC-II (Delhi and Uttar Pradesh), while the regional sources were dust (south-west (SW)), industrial, Cl-rich (north-west (NW)), SFC-I (east and south-east (SE)) and S-rich (SE).
... Meteorological parameters during the study period are presented in Fig. S1. Delhi receives emissions from a wide variety of local anthropogenic sources, including traffic, industries, power plants, brick kilns, bio-fuel burning, etc. (Guttikunda and Calori, 2013;Rai et al., 2020). In addition, long-range regional transport (hundreds of kilometers) of aerosols from upwind regions (e.g., Punjab, Haryana) also contributes to the air pollution of the study region during winter (Fig. S2a, Bikkina et al., 2019). ...
Article
It is well established that light-absorbing organic aerosols (commonly known as brown carbon, BrC) impact climate. However, uncertainties remain as their contributions to absorption at different wavelengths are often ignored in climate models. Further, BrC exhibits differences in absorption at different wavelengths due to the variable composition including varying sources and meteorological conditions. However, diurnal variability in the spectral characteristics of water-soluble BrC (hereafter BrC) is not yet reported. This study presents unique measurement hitherto lacking in the literature. Online measurements of BrC were performed using an assembled system including a particle-into-liquid sampler, portable UV-Visible spectrophotometer, and total carbon analyzer (PILS-LWCC-TOC). This system measured the absorption of ambient aerosol extracts at the wavelengths ranging from 300 to 600 nm with 2 min integration time and water-soluble organic carbon (WSOC) with 4 min integration time over a polluted megacity, New Delhi. Black carbon, carbon monoxide (CO), nitrogen oxides (NOx), and the chemical composition of non-refractory submicron aerosols were also measured in parallel. Diurnal variability in absorption coefficient (0.05 to 65 Mm-1), mass absorption efficiency (0.01 to 3.4 m-2 gC-1) at 365 nm, and absorption angstrom exponent (AAE) of BrC for different wavelength range (AAE300-400: 4.2-5.8; AAE400-600: 5.5-8.0; and AAE300-600: 5.3-7.3) is discussed. BrC chromophores absorbing at any wavelength showed minimum absorption during afternoon hours, suggesting the effects of boundary layer expansion and their photo-sensitive/volatile nature. On certain days, a considerable presence of BrC absorbing at 490 nm was observed during nighttime that disappears during the daytime. It appeared to be associated with secondary BrC. Observations also infer that BrC species emitted from the biomass and coal burning are more absorbing among all sources. A fraction of BrC is likely associated with trash burning, as inferred from the spectral characteristics of Factor-3 from the PMF analysis of BrC spectra. Such studies are essential in understanding the BrC characteristics and their further utilization in climate models.
... The study site Varanasi (25°28 0 N and 82°97 0 E; 82.2 m AMSL, Fig. 1) represents an urban environmental location over middle-Indo Gangetic Plain, characterized by multiple sources of aerosol mainly from road dust re-suspension, commercial activities, vehicular exhausts, and biomass/ waste burning (Ram and Sarin 2011;Dey et al., 2012;Guttikunda and Calori, 2013;Banerjee et al., 2015). The region characteristically experiences a humid subtropical climate with distinct seasonal variations. ...
Article
Short-term investigations of atmospheric pollutants (PM10, PM2.5, SO2, NO2, O3, and CO) were performed during the Diwali festival over Varanasi for a period of six years from 2011 to 2016. Aerosol Optical Depth (AOD) observed for the corresponding days of Diwali was found to be considerably much higher and even its value reached 2.0 for some Diwali years, which is basically almost 3-folds than the control days. The total scattering aerosol optical thickness as well as aerosol extinction co-efficient at 550 nm crossed the value of 1.0 in almost all the Diwali day cases. The associated meteorological conditions (low wind speed, declining temperature, lowered night-time boundary layer height, etc.) during the Diwali period leads to the detrimental accumulation of atmospheric pollutants near to the surface layer in Varanasi region. Moreover, PM10 and PM2.5 concentrations were recorded much higher than the safer limits set by NAAQS for 24-hour mean values throughout the period of study. The concentrations of PM10 and PM2.5 crossed beyond the safer limits and crossed 500 µg/m³ (in 2015) and 450 µg/m³ (in 2016) respectively, which is basically 5–6 times higher than the standard NAAQS limit. In comparison with the trace gases concentrations (e.g. SO2, NO2, O3, and CO) on control day, it was observed higher on the respective Diwali day. Satellite data derived from MODIS (Aqua and Terra) have also been taken into account to observe and verify the unpropitious effects of fireworks for the chosen case. MODIS true-color images show dense smoke plumes and haze over the entire Indo-Gangetic Plain (IGP) on Diwali days of 2011–2016 with its continuation in the following days. Proper assessment and regular monitoring is needed in order to mitigate the localized air pollution due to this kind of festival by the local scale authority to the top-level environmentalists.
... Geographic Information System (GIS) methods can improve the calculation of pollutant concentrations in built environments by incorporating these location-based datasets with better spatial resolution in the modeling process. Previous studies that utilized GIS and dispersion modeling indicate that pollution sources are better accounted for in spatial inventories of emissions [12] and pollution levels are linked with a spatial database that permits visualization on horizontal and vertical variations of vehicular traffic-induced air pollution [13]. However, these studies employed GIS techniques mainly in developing emission inventories and in data visualization. ...
Article
Air quality reports in the Philippines are primarily based on ground measurements from only 75 regional monitoring stations across the country. The sparse distribution of monitoring stations translates to the lack of a comprehensive and sufficient air quality information in urban areas. In Baguio City, one of the highly polluted cities in the Philippines, the ambient air quality condition is based on data from a single continuous monitoring station located in its Central Business District (CBD), failing to quantify pollution levels near roads. This study aims to provide reliable information on roadside air quality, particularly coarse particulate matter (PM10), in the CBD using Geographic Information Systems (GIS) and numerical modeling techniques. Vehicular traffic was identified as a significant contributor in the poor air quality in Baguio City, hence, an air dispersion model characterizing the effect of vehicular emissions was developed. The PM10 dispersion model combined with geostatistical techniques generated detailed roadside concentration estimates with low mean prediction errors (0.0003 to 0.0008 μg/m3) and low root mean square error (2.95 to 5.43 μg/m3). Results describe the spatio-temporal variations of roadside PM10 and indicate that high PM10 concentrations occur on roads with high vehicular emissions in northern CBD during nighttime conditions. Wind velocity variations have significant effects on the PM10 dispersion, as observed on the hourly pollution maps. As demonstrated in this study, integrating GIS, dispersion modeling and geostatistical techniques can address the information gap in reporting roadside air quality of urban areas with limited number of monitoring stations.
... The mean concentrations of PM 2.5 , for example, at Kharagpur 117 ± 79 [52], 203 ± 40 µg m −3 at Kanpur [53], 285 ± 87 µg m −3 at Varanasi [54], and 232 ± 131 µg m −3 at Delhi [55]. Moreover, refs [56,57] have also reported PM 2.5 concentrations of 123 µg m −3 and 121 µg m −3 in Delhi and Agra cities, respectively. The average BC mass concentrations were recorded as 9.46 ± 3.35 μg m −3 (5.06-19 µ g m −3 ) and 8.58 ± 1.60 μg m −3 (5.50-11.52 ...
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Black carbon (BC) and PM2.5 chemical characterizations are crucial for insight into their impact on the health of the exposed population. PM2.5 sampling was carried out over selected residential sites of Jamshedpur (JSR) and Kharagpur (KGP), east India, during the winter season. Seven selected elements (SO42−, Cl−, Na+, NO3−, K+, Ca2+, and Mg2+) were analyzed using ion chromatography (IC). Black carbon (BC) sampling was also done at two different sites in JSR and KGP to understand its correlation. The PM2.5 ionic species mass concentration in JSR was in the order of SO42− > Cl− > Na+ > NO3− > K+ > Ca2+ > Mg2+, whereas in KGP, it was SO42− > NO3− > Cl− > Na+ > K+ > Ca2+ > Mg2+. The back-trajectory analysis showed that most of the air masses during the study period originated from the Indo Gangetic Plain (IGP). The Pearson relations of BC-PM2.5 indicate a better positive correlation (r = 0.66) at KGP compared to JSR (r = 0.42). As shown in the diagnostic ratio analysis, fossil fuel combustion and wood burning account for 51.51% and 36.36% of the total energy consumption in JSR city, respectively. In KGP city, the apportionment of origin sources were fossil fuel and wood burning at 43.75% and 34.37%, respectively. This study provides the first inventory of atmospheric particulate-bound chemical concentrations and BC profiles in middle-east India and informs policymakers and scientists for further studies.
... Studies have demonstrated that food poisoning and carbon monoxide poisoning increase during power outages because people consume food that is spoiled (given fridges and freezers do not work) or use alternative power sources improperly [34]. The use of back-up generators [35] also contributes to poor air quality and carbon emissions [36]. It is concluded that communications following power outages are important in order to reduce health and safety risks [34]; communications could also link experiences to consideration of wider societal issues, for example energy security and climate change. ...
Article
Concerns about climate change and energy security, and related behaviour may be impacted by experiences such as flooding and power outages and we consider that impacts may be different for individual and social actions. Our first study, using online survey data from a quota sample in the UK (N = 1543) found that concerns about climate change and energy security differed for people who had recent power outage experience compared to those who did not; with small but significant effects. A mediation model analysis found that people who had experienced power outages were more likely to intend to engage in social energy saving behaviours, partially mediated by concerns about climate change and energy security. Our second study used survey data from a convenience sample in Mexico City (N = 661). Here a further mediation analysis indicated that people who had experienced higher levels of power outages or flooding were more likely to intend to engage in social energy saving behaviours. In aggregate no significant impacts of experiences on individual energy saving behaviours were found. We conclude that shared adverse experiences may promote prosocial interactions around environmental issues and that there is a key role for communications around environmental experiences in order to promote sustainable behaviour.
Chapter
Increasing anthropogenic emissions from industrial activities, biomass burning, and vehicles over South Asia have significantly affected the atmospheric loading and chemical composition of aerosols. These emissions do not only produce particulate carbonaceous species but are also the source of several gaseous species (e.g., CO, NOx, SO2, CH4, etc.) and volatile organic compounds in the atmosphere. In this book chapter, I present the abundance pattern of particulate matter (PM), carbonaceous species (EC and OC), and diagnostic ratios (OC/EC, K⁺/OC and ¹⁴C measurements, etc.) in ambient aerosols from Asian cities. However, more emphasis is given to the chemical characteristics of aerosols and carbonaceous species during various field observations and campaigns conducted over high-altitude sites (Manora Peak, located in the Himalaya and Mt Abu located in Rajasthan in Western India) and urban locations in the Indo-Gangetic plain (Kanpur, Hisar, and Allahabad) in India.
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As the carrier of human economic activities, the change of territorial space affects the level of regional carbon balance. Therefore, with regional carbon balance as the goal, this paper proposed a framework from the perspective of production-living-ecological space and took Henan Province of China as a study area for empirical research. Firstly, the study area established an accounting inventory that considers nature, society, and economic activities to calculate carbon sequestration/emission. Then, the spatio-temporal pattern of carbon balance was analyzed by ArcGIS from 1995 to 2015. Later, the CA-MCE-Markov model was used to simulate the production-living-ecological space pattern in 2035, and carbon balance in three future scenarios was predicted. The study showed that from 1995 to 2015, the living space gradually expanded, and the aggregation rose while the production space decreased. Carbon sequestration (CS) was less than carbon emission (CE) and presented an unbalanced state of negative income in 1995, while CS exceeded CE and showed a positive income imbalance in 2015. In 2035, living space has the highest carbon emission capacity under Natural Change Scenario (NC), while ecological space has the highest carbon sequestration capacity under Ecological Protection Scenario (EP), and production space has the highest carbon sequestration capacity under Food Security Scenario (FS). The results are crucial for understanding the carbon balance changes in territorial space and supporting regional carbon balance goals in the future.
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Living in the healthy atmospheric environment is becoming the basic necessity of every human being. As unhealthy atmospheric environment is becoming threat to life. Due to the tremendous increase in the air pollution level, it is difficult to get clean and fresh air to inhale which is very important for the survival of human life. This menacing problem needs to address globally for the welfare of the present and future generation. Thus, keeping all this in mind, a proper study was carried out in the city of prayagraj to address the increased level of air pollution. This paper presents the management and mitigation plan of praygraj city based upon emission level and the results are compared on the basis of spatial distribution. Several major sources of pollution in the city have been noted such as from Domestic, Vehicular Pollution, Road dust, Municipal solid waste (MSW) and Brick kiln. There are several air pollutantsin the atmosphere which have negative impacts on human health but we have considered only particulate matter such as PM10 and PM2.5 for our study. The findings of this study will help the administration, Municipal Corporation and various stake holders of the city to take targeted measures locality wise towards pollution control depending upon pollutants concentration and exposure area-wise. It will also raise public awareness about pollutant levels in their area.
Article
This paper presents the characteristics of ambient particulate matter (PM), resuspendable road dust and PM mass deposition in the human respiratory tract during preconstruction and construction phases. PM Emission Rates (PMER) due to resuspension and spatiotemporal distribution were estimated and compared on construction roads (CR) and non-construction roads (NCR) for both phases. The construction phase monitoring results demonstrated that the silt load (SL) and PMER at CR (SL = 26–47 g/m², PM10ER = 18.1–43.8 g/VKT, PM2.5ER = 4.3–10.6 g/VKT) were significantly high when compared to NCR (SL = 3.0–12.5 g/m², PM10ER = 0.3–7.5 g/VKT, PM2.5ER = 0.1–1.8 g/VKT). Preconstruction phase results showed 15 to 20 times lesser values. Spatial and temporal variation studies showed that maximum PM concentrations (PM10 = 270.1, PM2.5 = 71.8, PM1 = 56.3 μg/m³) were found during night at construction roads due to the movement of heavy-duty vehicles carrying excavated earth overnight. Between 0 and 100 m length of road on either side of the construction sites, average PM10 concentrations were greater than 250 μg/m³. Similarly, for distance between 100 and 200 m, 200–400 m and 400–500 m, the PM10 values ranged between 200 and 250 μg/m³, 150–200 μg/m³ and 100–150 μg/m³ respectively. The current study results clearly indicated that resuspension of road dust due to movement of heavy duty trucks highly influence the PM concentrations in the surrounding environment of a construction site. The MPPD model results indicated that the total deposition fraction of PM10 in construction workers airway during the construction phase was 74–78%, followed by PM2.5(23–54%) and PM1(20–25%). Integration of sustainable practices, use of pollution control technologies and implementation of policies at a local scale are the way forward to mitigate the pollution from construction activities.
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Fuelwood collection by poor people is often cited as the most important cause of deforestation in developing countries. Using household survey data from India, we show that households located farther away from the forest spend more time in fuelwood collection. These households are likely to sell more fuelwood and buy less. That is, reduced access to forests increases fuelwood collection and sale. This counter-intuitive behavior is triggered by the higher fixed costs of households living farther from the forest arising from larger travel times. By combining two different datasets, we can quantify net fuelwood sales out of a village. We show that a fifth of the fuelwood collected is consumed outside of rural areas, in nearby towns and cities. Our estimates suggest that fuelwood burning may account for roughly 14%–20% of the typical daily PM2.5 load in a city like Delhi.
Article
Vehicular emissions are the major source of air quality deterioration in Indian megacities. However, there is uncertainty in vehicular emission estimation due to the paucity of vehicular use and travel characteristics, and there is no specific methodology to assess the same. Thus, this study presents a methodology to capture the urban in-use vehicular characteristics. Additionally, it evaluates current vehicular emissions in Mumbai and estimates future emission levels for the year 2030, taking into account various policy interventions. Data for the study were collected via questionnaire surveys at fuel stations across Greater Mumbai – a first in western India. Exhaust and non-exhaust vehicular emissions were developed using the “bottom-up” methodology. Six scenarios were tested for exhaust vehicular emissions and energy consumption under various policy interventions. Monte-Carlo Simulations (MCS) were carried out to find the uncertainties in the vehicular emission estimation. Results showed that approximately 66% of the registered vehicles ply on Mumbai roads, and the on-road fuel efficiency is 12–33% less than the reported lab-based studies. Our study findings suggest that conducting surveys at three fuel stations is adequate for determining urban in-use vehicular characteristics with <5% bias. Reduction in vehicular emissions calls for stringent norms for private passenger vehicles and regulation of non-exhaust vehicular emissions. Given projected vehicular emissions for 2030, urban cities like Mumbai will have to inevitably replace conventional vehicles with electric vehicles to achieve the Paris agreement, which is to limit global warming well below 2 °C.
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Investigating seasonal variation in health helps us understand interactions between population, environment, and disease. Using information on birth month and year, survival status within the first year of life, and age at death (if applicable) of more than 330,000 children observed in four rounds of India’s Demographic and Health Surveys, I estimate period mortality rates between birth and age one (1m0) by calendar month. Relative to spring months, infant mortality is higher in the summer, monsoon, and winter months. If spring mortality conditions had been prevalent throughout the year, mortality below age one would have been lower by 11.4 deaths per 1,000 in the early 1990s and 3.7 deaths per 1,000 in the mid-2010s. Seasonal variation in infant mortality has declined overall but remains higher among disadvantaged children. The results highlight the multiple environmental health threats that Indian infants face and the short time of year when these threats are less salient.
Chapter
This chapter describes and quantifies aerosol and precursor gas emissions from industrial processes and the natural world. The procedure for developing industrial emission inventories is described in terms of activity data and emission factors for point sources, mobile sources, and distributed sources. Emissions from the natural world are described and quantified, including mineral dust, fires, sea spray, and precursor gases like sulfur dioxide and organic compounds from marine and terrestrial sources. Global total emissions are presented for each of the main aerosol chemical components. The chapter presents estimates of long-term historical variations in emissions since the preindustrial period and projections of future emissions based on economic and social scenarios. The chapter concludes with a summary of the challenges involved in representing emissions in models.
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This study presents a method for “downscaling” aggregated global emissions of CO, NOx, and PM2.5 based on georeferenced information (spatial proxies). We distribute ECLIPSE-CLE emissions for Quito, Ecuador, in 2015 and 2017. The study area is a grid of 0.5 × 0.5 km² cells over a 110 × 110 km² area. The emission sectors (proxies in parenthesis) are agricultural (land-use maps), domestic (land-use and population density), energy, industry, and waste treatment (point source location from local inventory), and transport (population, vehicle traffic, and road density). Emission distribution quality is satisfactorily evaluated (graphically and statistically) by implementing them in the UBM model and comparing modeled concentrations with observations. This study also explores an alternative proxy set-up for main road emissions based on road density, which, for some modeling sites, results in a better agreement with the observations. Finally, this methodology is applied for comparing air pollution due to two urban growth types for Quito in 2040: sprawl and densification. Both scenarios lead to lower concentrations than in 2017, except for O3. Although the two scenarios attain similar concentrations, urban sprawl presents, in general, noticeably higher values for NOx and NO2.
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Fog is a major hazard in wintertime over India, particularly in the Indo‐Gangetic Plains, leading to significant impacts for transport and human health. Using 3‐hourly surface observations, from 69 sites across India, all fog and dense fog events between 2000 and 2020 are identified. For each event, the main fog formation mechanism is objectively categorised using a classification algorithm, distinguishing between radiation, advection, evaporation, precipitation or cloud‐base lowering fog types. In contrast to the findings of other international studies, radiation fog dominates as the most common fog type at the vast majority of locations in India, accounting for 68.1 % of all fog events and 70.0 % of dense fog events. Statistically significant positive trends are seen in the frequency of all fog events at Delhi, Lucknow and Patna, in the Indo‐Gangetic plains, between 1997/1998 and 2018/2019, dominated by comparable statistically significant positive trends in radiation fogs. Western Disturbances (WD) are often linked to the formation of fog in India. Using a climatology of WDs, we show that 46.9 % of radiation fog onsets in Delhi in December and January, the primary fog months, happened in conjunction with an active WD event. Conversely, only 32.3 % of WDs during these same months coincided with the onset of a radiation fog event. WD‐related radiation fog events are shown to cluster into three distinct groups, with WD centres located to the north‐west (51.4 % of cases), south‐west (13.3 %) and east (35.2 %) of Delhi. Each cluster is shown to have coherent and distinct near‐surface characteristics which are conducive to fog formation. Trends in WD frequency cannot fully account for the observed trends in fog events. We argue that the fog trends are more likely the result of a complex interaction between urban expansion and the associated rapid change in aerosol loading, resulting in impacts on radiation balance, microphysics and heat‐island processes.
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The study aimed to analyze the long-term trend of four criteria pollutants (PM2.5 and NO2 during 2006–2019; SO2 and CO during 2000–2019) and the effect of pollution control measures in Delhi. The hourly data of PM2.5 and NO2 was taken from six air quality monitoring stations while SO2 and CO data was retrieved from MERRA reanalysis (Modern-Era Retrospective Analysis for Research and Applications) products. PM2.5 and NO2 datasets from six monitoring stations were divided into three categories based on their years of data collection. At all the categories of sites, both the pollutants showed three distinct trends including a decreasing trend which was common from 2015 to 2019. The annual average trends of SO2 and CO showed two distinct trends including an increasing trend which was common from 2013 to 2019. The study analyzed various control policies, among them Compressed Natural Gases (CNG) implementation in December 2002, is a historic measure which was followed by 13.5% decrease in particulate matter trend and 20% increase in NO2 trend during 2002–2005 while 7% decrease in CO trend during 2003–2005. The introduction of ultralow sulfur diesel and closure of coal based thermal power plants reduced SO2 levels significantly during 2011–2012.
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Particulate matter (PM) concentrations and aerosol optical depth (AOD) are measured and correlated simultaneously using a high-volume sampler and a MICROTOPS-II Sunphotometer, respectively. The present work deals with the characteristics of particulate matter (PM 1 , PM 2.5 , and PM 10 ) over Varanasi, from April 2019 to March 2020. Daily variation, as well as seasonal variation, reveals the dominancy of fine-mode particles over the Varanasi region in the winter season and the dominancy of coarse-mode particles in the summer season, which was further confirmed by calculating the ratio between particulate matter (PM 1 /PM 10 and PM 2.5 /PM 10 ). This ratio was discovered to be lowest in the summer and highest in the winter. Annual mean concentrations of PM 1 , PM 2.5 , and PM 10 are found to be 93.91, 111.34, and 180.70 μgm ⁻³ , respectively. The seasonal variation shows relatively a higher concentration of PM 1 , PM 2.5 , and PM 10 in the winter season, which may be due to stable meteorological conditions and increased biomass burning in winter. Diurnal and seasonal variations in AOD were also studied during this period. A large and small value of AOD represents the dominancy of fine particles over coarse particles. At 500 nm, maximum (1.17) and minimum (0.44) AODs were measured in December and August of 2019, respectively. There was a statistically significant correlation between PM particles (PM 1 , PM 2.5 , and PM 10 ) and AOD. Elemental analysis shows that fluorine and carbon are the major elements that were observed in selected samples during the post-monsoon and winter season using SEM-EDX analysis.
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The Indo-Gangetic Plain (IGP) is one of the dominant sources of air pollution worldwide. During winter, the variations in planetary boundary layer (PBL) height, driven by a strong radiative thermal inversion, affect the regional air pollution dispersion. To date, measurements of aerosol-water vapor interactions, especially cloud condensation nuclei (CCN) activity, are limited in the Indian subcontinent, causing large uncertainties in radiative forcing estimates of aerosol-cloud interactions. We present the results of a one-month field campaign (February-March 2018) in the megacity, Delhi, a significant polluter in the IGP. We measured the composition of fine particulate matter (PM1) and size-resolved CCN properties over a wide range of water vapor supersaturations. The analysis includes PBL modeling, backward trajectories, receptor models and fire spots to elucidate the influence of PBL and air mass origins on aerosols. The aerosol properties depended strongly on PBL height and a simple power-law fit could parameterize the observed correlations of PM1 mass, aerosol particle number and CCN number with PBL height, indicating PBL induced changes in aerosol accumulation. The low inorganic mass fractions, low aerosol hygroscopicity and high externally mixed weakly CCN-active particles under low PBL height (<100 m) indicated the influence of PBL on aerosol aging processes. In contrast, aerosol properties did not depend strongly on air mass origins or wind direction, implying that the observed aerosol and CCN are from local emissions. An error function could parameterize the relationship between CCN number and supersaturation throughout the campaign.
Article
Methods to estimate absorption of brown carbon (BrC), a significant fraction of atmospheric absorption, rely on estimating the difference between total measured absorption at near-UV, and that of black carbon (BC). Extrapolation of absorption measured at near-IR wavelengths (assumed exerted by BC alone) use different assumptions of the wavelength dependence of absorption Ångström exponent (AAEBC). Here, we develop an improved method exploiting real-time multi-wavelength absorption and particle count measurements in a Mie based optimization framework, incorporating spectral observational constraints (measured absorption at 880 nm and AAE880-660.). An optimization approach, using a Mie model with core-shell and core-gray shell mixing schemes, is used to derive BC size distribution parameters (absorbing core diameter and scattering shell thickness). Goodness of fit (Mie optimization model vs. measurement) was R = 0.77–0.94 (near-IR absorption) and within 4%–30% for BrC estimation. A sensitivity analysis of input parameters (BC geometric standard deviation and refractive index) bounded estimated BrC of 32%. Application to a polluted urban site (Delhi) and a regional background site (Darjeeling) estimated BrC absorption (% contribution) at 370 nm as 18–117 Mm⁻¹ (15%–29%) and 2–12 Mm⁻¹ (5%–21%), respectively. Estimated BrC absorption was larger at the regional background site (Darjeeling) but smaller at the polluted site (Delhi) when compared to constant AAE and two-component approaches. Method efficacy is reinforced through larger estimated BrC absorption at Delhi coinciding with agricultural stubble burning periods in North India. The developed method uses multi-wavelength absorption observational constraints to improve the robustness of BrC estimation.
Chapter
Environmental pollution is a crucial problem that the world is confronting in present times. In addition to land and water, contamination of the air is escalating with each passing day. With the modernization and advancement of industrial processes, the environment is loaded with various kinds of pollutants like sulfur dioxide, nitrogen oxides, ozone, particulate matter, carbon monoxide, hydrocarbons, chlorofluorocarbons, and many others. The presence of various pollutants in the air, specifically toxic gases, not only leads to the degradation of human health and plant productivity but also results in the destruction of biodiversity. Thus due to the increase in pollutants and their concentrations, the development of technology and instruments that are capable of treating and preventing it properly is necessary. Nanotechnology put forward a lot of potential and technologies to improve the current environmental conditions that have stimulated the development of cost-effective technologies which are used for air pollution detection, pollution monitoring, and remediation. First, this chapter sheds some light on the current scenario of air pollution, and chiefly focuses on the compilation of available high-throughput innovations that are useful for countering air pollution in three basic steps (purification and remediation, contaminant sensing, and detection, and pollution prevention). Moreover, to control increasing pollution levels, both at the source and the sink, and for well-timed monitoring of pollutants in the air, better developments in the existing technologies and cutting-edge advanced innovations are necessary.
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Cyclists’ exposure to air and noise pollution is a growing field of study. A situation of transport injustice has been largely documented, showing that cyclists are more exposed than other road users to pollutions they do not produce themselves. Modeling cyclists’ exposures became an important issue to understand how to reduce them and how to include that issue in planning. However, many gaps are currently characterizing the field of cyclists’ exposure modeling studies: methodological discrepancy, few comparison analyses, cities in the Global South and noise exposure are both under-studied. This study fits in these gaps by proposing a comparative analysis of seven cities (Paris, Lyon, Copenhagen, Delhi, Mumbai, Montreal and Toronto) considering both noise and exposure to nitrogen dioxide (NO2) and applying a uniform methodology allowing for comparison and generalization of the results. The built models are critically analyzed, and used to map cyclists’ exposure. These maps of relative potential exposure provide interesting perspectives for planning at both the regional and local levels. We found a weak correlation between cyclists’ exposure to environmental noise and NO2. Noise depends more on characteristics of the micro-scale environment in which exposure occurs than NO2. Thus, planning to reduce cyclists’ exposure to noise can have significant effects. For NO2, the micro-scale environment only has a significant impact in Mumbai and Delhi. However, our results suggest that it might be difficult to systematically combine several dimensions of the quality of a bicycle network such as straightness, connectivity, safety, and reduced air and noise exposure.
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Sporadic efforts have been introduced to control emissions in Delhi, but the air quality has declined further due to the rapid development of different sectors. In this study, the impact of various mitigation scenarios on air quality for PM10, ozone, and its precursors are studied using a chemical transport model, namely WRF-Chem. The Emission Database for Global Atmospheric Research emission inventory was modified and introduced into the WRF-Chem model to assess the impact of selected emission control scenarios on different sectors. The simulations were conducted with reduced emissions for these sectors over the study domain: (a) implementation of Bharat Stage—VI norms in the transport sector, (b) conversion of fuel from coal to natural gas in the energy sector, and (c) fuel shift to LPG in the residential sector. The transport sector noted a decrease of 4.9% in PM10, 44.1% in ozone, and 18.9% in NOx concentrations with emission reduction measures. In the energy sector, a marginal reduction of 3.9% in NOx concentrations was noted, and no change was observed in PM10 and ozone concentrations. In the residential sector, a decrease of 8% in PM-10, 47.7% in ozone, and 49.8% in NOx concentrations were noted. The VOC-to-NOx ratios were also studied, revealing the ozone production over the study domain was mostly VOC-limited. As the inclusion of control measures resulted in varying levels of reduction in pollutant concentrations, it was also studied in the context of improving the air quality index. The WRF-Chem model can be successfully implemented to study the effectiveness of any regulated control measures.
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We propose a carbon tax policy for Delhi—the most polluted capital globally—which will fundamentally change the energy mix of Delhi’s economy toward clean, green energy and guarantee universal access to electricity, transport, and food, up to a certain amount. Any carbon mitigation strategy needs to alter our dependence on fossil fuels, requiring a systemic overhaul of its energy mix. Implementing a carbon tax will mitigate emissions and mobilise revenue for our proposed redistributive program: Right to Food, Energy, and Travel (RFET). The policy is designed to advocate for the ‘poor over the rich’ to compensate for the ‘rich hiding behind’ the poor by emitting the majority of carbon and pollutants. Using input–output analysis, we estimate the class-wise distribution of carbon emissions in Delhi. We find that the necessary tax would be US$112.5 per metric ton of carbon dioxide in order for this program to work. The free entitlement of fuel and electricity per household comes out to be 2040 kWh per annum, and there is an annual universal travel pass of US$75 per person for use in public transport and an annual per capita availability of food of US$205. JEL Codes: Q43, Q48, Q52, Q58
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This case study of the brick kiln sector in Chennai shows that workers are in a "mild" situation of debt bondage, have to work for long hours, and very often put their children to work as well. However, they are paid wages that are very close to the rates fixed by the government and the system of advance payment is endorsed by both workers and kiln owners and the former see it as a means to social mobility. Only the coming together of employers, unions, NGOs, public authorities and job brokers can help break the debt bondage.
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Although there is general public approval of the improvements in Delhi's air quality in recent years, the process by which this change was brought about has been criticised. A common perception is that air quality policies were prescribed by the Supreme Court, and not by an institution with the mandate for making environmental policy. A careful review of the policy process in Delhi suggests otherwise. We find that the government was intimately involved in policy-making and that the main role of the Supreme Court was to force the government to implement previously announced policies. A good understanding of what happened is essential, as the Delhi experience for instituting change has become a model for other Indian cities as well as neighbouring countries.
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As part of the System of Air quality Forecasting and Research (SAFAR) project developed for air quality forecasting during the Commonwealth Games (CWG) - 2010, a high resolution Emission Inventory (EI) of PM 10 and PM 2.5 has been developed for the metropolitan city Delhi for the year 2010. The comprehensive inventory involves detailed activity data and developed for a domain of 70 km × 65 km with a 1.67 km × 1.67 km resolution covering Delhi and surrounding region using Geographical Information System (GIS) technique. The major sectors considered are, transport, thermal power plants, industries, residential and commercial cooking along with windblown road dust which is found to play a major role for Delhi environment. It has been found that total emissions of PM 10 and PM 2.5 including wind blown dust over the study area are found to be 236 Gg yr -1 and 94 Gg yr -1 respectively. The contribution of windblown road dust is found to be as high as 131 Gg yr -1 for PM 10.
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Brick manufacturing is the fastest-growing industrial sector in Bangladesh and among the top three sectors, along with vehicle exhaust and resuspended road dust, contributing to the air pollution and health problems in Dhaka. The brick manufacturing in the Greater Dhaka region, from ~1,000 brick kilns spread across six districts, is confined to the winter season (October to March) as current technologies do not allow production during the monsoon. The total emissions are estimated at 23,300 t of PM2.5, 15,500 t of sulfur dioxide (SO2), 302,000 t of carbon monoxide (CO), 6,000 t of black carbon, and 1.8 million tons of CO2 emissions from these clusters, to produce 3.5 billion bricks per year, using energy-inefficient fixed chimney bull trench kiln technology and predominantly using coal and agricultural waste as fuel. The associated health impacts largely fall on the densely populated districts of Dhaka Metropolitan Area (DMA), Gazipur, and Narayanganj. Using the Atmospheric Transport Modeling System dispersion model, the impact of brick kiln emissions was estimated over DMA—ranging from 7 to 99 μg/m3 (5th and 95th percentile concentration per model grid) at an average of 38 μg/m3; and spatial contributions from the surrounding clusters—with 27 % originating from Narayanganj (to the south with the highest kiln density), 30 % from Gazipur (to the north with equally large cluster spread along the river and canals), and 23 % from Savar. The modeling results are validated using evidence from receptor modeling studies conducted in DMA. An introduction of emerging vertical shaft combustion technology can provide faster benefits for public health in DMA and reduce climate precursor emissions by selecting the most influential clusters discussed in this paper.
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Fine particle organic carbon in Delhi, Mumbai, Kolkata, and Chandigarh is speciated to quantify sources contributing to fine particle pollution. Gas chromatography/mass spectrometry of 29 particle-phase organic compounds, including n-alkanes, polycyclic aromatic hydrocarbons (PAHs), hopanes, steranes, and levoglucosan along with quantification of silicon, aluminum, and elemental carbon are used in a molecular-marker based source apportionment model to quantify the primary source contributions to the PM2.5 mass concentrations for four seasons in three sites and for the summer in Chandigarh. Five primary sources are identified and quantified: diesel engine exhaust, gasoline engine exhaust, road dust, coal combustion, and biomass combustion. Important trends in the seasonal and spatial patterns of the impact of these five sources are observed. On average, primary emissions from fossil fuel combustion (coal, diesel, and gasoline) are responsible for about 25–33% of PM2.5 mass in Delhi, 21–36% in Mumbai, 37–57% in Kolkata, and 28% in Chandigarh. These figures can be compared to the biomass combustion contributions to ambient PM2.5 of 7–20% for Delhi, 7–20% for Mumbai, 13–18% for Kolkata, and 8% for Chandigarh. These measurements provide important information about the seasonal and spatial distribution of fine particle phase organic compounds in Indian cities as well as quantifying source contributions leading to the fine particle air pollution in those cities.
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Air pollution is a major environmental problem in urban areas worldwide. Delhi, the capital city of India, is no exception to the universal pattern of deteriorating urban air quality with concentration of pollutants being well above the recommended WHO levels. The magnitude and urgency of the problem as a global environmental issue needs a systematic understanding of the potential causes of pollution and their contribution to air quality. In the present study, ambient air quality data (1987–2006) of SO2, NO2, SPM, and RSPM were analyzed to asses the changing air quality in the study area and to evaluate the effect of measures taken to control it. The primary data were collected from 1,583 households to examine the relationship between outdoor and indoor pollution level. Based on the data, the current study concludes that despite the implementation of different pollution-controlling measures, the pollutants, especially the particulate pollutants, were well above the standard limits set by CPCB. Integration between technological and social approach of urban planning is required to mitigate and manage urban environmental problems in sustainable manner.
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A study of the winter time size distribution and source apportionment of total suspended particulate matter (TSPM) and associated heavy metal concentrations have been carried out for the city of Delhi. This study is important from the point of view of implementation of compressed natural gas (CNG) as alternate of diesel fuel in the public transport system in 2001 to reduce the pollution level. TSPM were collected using a five-stage cascade impactor at six sites in the winters of 2005–06. The results of size distribution indicate that a major portion (~40%) of TSPM concentration is in the form of PM0.7 (
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A decentralized emission inventories are prepared for road transport sector of India in order to design and implement suitable technologies and policies for appropriate mitigation measures. Globalization and liberalization policies of the government in 90's have increased the number of road vehicles nearly 92.6% from 1980–1981 to 2003–2004. These vehicles mainly consume non-renewable fossil fuels, and are a major contributor of green house gases, particularly CO2 emission. This paper focuses on the statewise road transport emissions (CO2, CH4, CO, NOx, N2O, SO2, PM and HC), using region specific mass emission factors for each type of vehicles. The country level emissions (CO2, CH4, CO, NOx, N2O, SO2 and NMVOC) are calculated for railways, shipping and airway, based on fuel types. In India, transport sector emits an estimated 258.10 Tg of CO2, of which 94.5% was contributed by road transport (2003–2004). Among all the states and Union Territories, Maharashtra's contribution is the largest, 28.85 Tg (11.8%) of CO2, followed by Tamil Nadu 26.41 Tg (10.8%), Gujarat 23.31 Tg (9.6%), Uttar Pradesh 17.42 Tg (7.1%), Rajasthan 15.17 Tg (6.22%) and, Karnataka 15.09 Tg (6.19%). These six states account for 51.8% of the CO2 emissions from road transport.
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India is used as a case study in reviewing the application of receptor models for source apportionment. India has high concentrations of airborne particulate matter, and the application of effective abatement measures is a high priority, and demands confidence in the results of source apportionment studies. The many studies conducted are reviewed, and reveal a very wide range of conclusions, even for the same city. To some degree these divergences may be the result of using different sampling locations and/or seasons, but to a large extent differences probably arise from methodological weaknesses. The assignment of factors from multivariate receptor models to specific source categories is in many cases highly questionable as factors often include combinations of chemical constituents that are of low plausibility. This ambiguity in terms of presence of tracer elements may be the result of genuine collinearity of diverse sources, or more probably arises from methodological problems. Few studies have used either organic molecular markers or chemical mass balance (CMB) models, and there is a shortage of data on locally-derived emission source profiles, although recent work has begun to remedy this weakness. The conclusions include a number of recommendations for use in design of future studies.
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Air pollution is a major contributor to several respiratory problems, it affects the whole population in general but children are more susceptible. Exposure to automobile exhaust is associated with increased respiratory symptoms and may impair lung function in children. In view of this, the study was conducted among the children of Delhi, the capital city of India, where ambient air quality was much above the National Ambient Air Quality Standards. The study was conducted in children aged 9–17 years. Pulmonary function test was carried out following the guideline of American Thoracic Society using a portable, electronic spirometer. Air quality data was collected from Central and State Pollution Control Boards. In addition, the level of particulate matter in indoor air was measured by portable laser photometer. Lung function was reduced in 43.5% schoolchildren of the urban area compared with 25.7% of control group. The urban children had increased prevalence of restrictive, obstructive, as well as combined type of lung functions deficits. Besides higher prevalence, the magnitude of lung function deficits was also much more in them. After controlling potential confounders like season, socioeconomic conditions and ETS, PM10 level in ambient air was found to be associated with restrictive (OR= 1.35, 95% CI 1.07–1.58), obstructive (OR=1.45, 95% CI 1.16–1.82), and combined type of lung function deficits (OR=1.74, 95% CI 1.37–2.71) in children. Spearman's rank correlation test reaffirmed the association. The study confirms that the level of air pollution is affecting the children.
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This paper presents PM10 fugitive dust emission factors for a range of vehicles types and examines the influence of vehicle and wake characteristics on the strength of emissions from an unpaved road. Vertical profile measurements of mass concentration of the passing plumes were carried out using a series of 3 instrumented towers. PM10 emission fluxes at each tower were calculated from knowledge of the vertical mass concentration profile, the ambient wind speed and direction, and the time the plume took to pass the towers. The emission factors showed a strong linear dependence on speed and vehicle weight. Emission factors (EF=grams of PM10 emitted per vehicle kilometer traveled) ranged from approximately EF=0.8×(km h−1) for a light (∼1200 kg) passenger car to EF=48×(km h−1) for large military vehicles (∼18 000 kg). In comparison to emission estimates derived using US EPA AP-42 methods the measured emission factors indicate larger than estimated contributions for speeds generally>10–20 km h−1 and for vehicle weights>3000 kg. The size of a wake created by a vehicle was observed to be dependent on the size of the vehicle, increasing roughly linearly with vehicle height. Injection height of the dust plume is least important to long-range transport of PM10 under unstable conditions and most important under stable atmospheric conditions.
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Cookstoves in developing countries are individually small, but so numerous that, depending on emission factors, they could be significant influences on global and regional carbon monoxide (CO) inventories. This paper presents a new database of CO emission factors for commonly used cookstoves in developing countries. The emission factors were determined using a carbon balance approach for 56 types of fuel/stove combinations in China and India. These include various stoves (e.g., traditional, improved, mud, brick, and metal, with and without chimney) using animal dung, different species of crop residues and wood, root fuel, charcoal, kerosene, and several types of coals and gases. The chosen fuel/stove combinations represent a large fraction of the total in developing countries. Thus, the database can be used to improve estimates of CO emission inventories. The CO emission factors ranged widely, from 3.0 × 10−2 g/kg for the coal gas/traditional stove to 2.8 × 102 g/kg for the charcoal/Angethi stove, nearly 4 orders of magnitude. Since stove efficiencies and fuel energy contents were measured simultaneously, CO emission factors on the basis of a standard cooking task (energy delivered) were also determined and reported in this paper. Task-based emission factors are particularly useful for comparing the air pollution potential of different fuel/stove combinations and assessing the impacts of substitutions.
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Vehicles affect the concentrations of ambient airborne particles through exhaust emissions, but particles are also formed in the mechanical processes in the tire-road interface, brakes, and engine. Particles deposited on or in the vicinity of the road may be re-entrained, or resuspended, into air through vehicle-induced turbulence and shearing stress of the tires. A commonly used term for these particles is road dust . The processes affecting road dust emissions are complex and currently not well known. Road dust has been acknowledged as a dominant source of PM10 especially during spring in the sub-arctic urban areas, e.g. in Scandinavia, Finland, North America and Japan. The high proportion of road dust in sub-arctic regions of the world has been linked to the snowy winter conditions that make it necessary to use traction control methods. Traction control methods include dispersion of traction sand, melting of ice with brine solutions, and equipping the tires with either metal studs (studded winter tires), snow chains, or special tire design (friction tires). Several of these methods enhance the formation of mineral particles from pavement wear and/or from traction sand that accumulate in the road environment during winter. When snow and ice melt and surfaces dry out, traffic-induced turbulence makes some of the particles airborne. A general aim of this study was to study processes and factors underlying and affecting the formation and emissions of road dust from paved road surfaces. Special emphasis was placed on studying particle formation and sources during tire road interaction, especially when different applications of traction control, namely traction sanding and/or winter tires were in use. Respirable particles with aerodynamic diameter below 10 micrometers (PM10) have been the main concern, but other size ranges and particle size distributions were also studied. The following specific research questions were addressed: i) How do traction sanding and physical properties of the traction sand aggregate affect formation of road dust? ii) How do studded tires affect the formation of road dust when compared with friction tires? iii) What are the composition and sources of airborne road dust in a road simulator and during a springtime road dust episode in Finland? iv) What is the size distribution of abrasion particles from tire-road interaction? The studies were conducted both in a road simulator and in field conditions. The test results from the road simulator showed that traction sanding increased road dust emissions, and that the effect became more dominant with increasing sand load. A high percentage of fine-grained anti-skid aggregate of overall grading increased the PM10 concentrations. Anti-skid aggregate with poor resistance to fragmentation resulted in higher PM levels compared with the other aggregates, and the effect became more significant with higher aggregate loads. Glaciofluvial aggregates tended to cause higher particle concentrations than crushed rocks with good fragmentation resistance. Comparison of tire types showed that studded tires result in higher formation of PM emissions compared with friction tires. The same trend between the tires was present in the tests with and without anti-skid aggregate. This finding applies to test conditions of the road simulator with negligible resuspension. Source and composition analysis showed that the particles in the road simulator were mainly minerals and originated from both traction sand and pavement aggregates. A clear contribution of particles from anti-skid aggregate to ambient PM and dust deposition was also observed in urban conditions. The road simulator results showed that the interaction between tires, anti-skid aggregate and road surface is important in dust production and the relative contributions of these sources depend on their properties. Traction sand grains are fragmented into smaller particles under the tires, but they also wear the pavement aggregate. Therefore particles from both aggregates are observed. The mass size distribution of traction sand and pavement wear particles was mainly coarse, but fine and submicron particles were also present. Moottoriajoneuvot päästävät hiukkasia ilmaan pakokaasujen mukana, mutta niitä muodostuu myös mekaanisissa prosesseissa tien pinnan ja renkaan vuorovaikutuksessa, jarruissa ja moottorissa. Lisäksi hiukkaset, jotka ovat laskeutuneet tien pinnalle tai sen lähettyville voivat nousta ilmaan uudelleen ajoneuvojen aiheuttamien ilmavirtojen tai renkaiden nostattamina (resuspensio). Yleistermi näille hiukkasille on katupöly . Katupölyn muodostumiseen ja päästöihin vaikuttavat prosessit ovat monimutkaisia ja tällä hetkellä huonosti tunnettuja. Katupöly muodostaa erityisesti keväisin merkittävän osan keuhkohengitettävistä PM10 hiukkasista maapallon pohjoisilla alueilla kuten Skandinaviassa, Pohjois-Amerikassa ja Japanissa. Korkeat katupölypitoisuuksien on esitetty olevan seurausta lumisista ja jäisistä talviolosuhteista, joiden takia liikenteessä on käytettävä liukkaudentorjuntaa. Liukkaudentorjuntamenetelminä käytetään esimerkiksi talvihiekoitusta ja teiden suolausta. Lisäksi autoissa käytetään joko nastallisia tai nastattomia talvirenkaita (kitkarenkaita), joiden suunnittelussa on erityisesti kiinnitetty huomiota renkaiden pitoon liukkaissa talviolosuhteissa. Useat liukkaudentorjuntamenetelmistä lisäävät mineraalihiukkasten muodostumista tien päällysteen tai hiekoitushiekan kulumatuotteina. Muodostuneet hiukkaset kerääntyvät tieympäristöön talven aikana. Keväällä kun lumi ja jään sulaa ja pinnat kuivuvat, hiukkasia nousee ilmaan merkittäviä määriä mm. liikenteen aiheuttamien ilmavirtauksien nostamina. Yleisenä tavoitteena tutkimuksessa oli selvittää katupölyn muodostumiseen vaikuttavia tekijöitä ja prosesseja päällystetyillä pinnoilla. Erityisenä painopisteenä oli tutkia hiukkasten muodostumista tien pinnan ja renkaan vuorovaikutuksessa, käytettäessä talvirenkaita ja hiekoitusmursketta. Mittausten kohteena olivat keuhkohengitettävät hiukkaset, joiden aerodynaaminen halkaisija on alle 10 mikrometriä (PM10), mutta myös muita hiukkaskokoluokkia tutkittiin. Tutkimuksessa selvitettiin seuraavia kysymyksiä: i) Miten talvihiekoitus ja hiekoitusmateriaalin fysikaaliset ominaisuudet vaikuttivat katupölyn muodostumiseen? ii) Miten nastoitetut talvirenkaat erosivat pölyn muodostumisen osalta nastoittamattomista talvirenkaista (kitkarenkaista)? iii) Mikä oli katupölyn koostumus ja lähteet testiradalla sekä keväisen katupölyepisodin aikana Suomessa? iv) Mikä oli tien ja renkaan vuorovaikutuksessa syntyneiden hiukkasten kokojakauma? Tutkimukset suoritettiin testiradalla ja kaupunkiolosuhteissa. Testiradalla saatujen koetulosten perusteella hiekoitus lisäsi PM10-katupölyn muodostumista ja että päästöt kasvoivat hiekoitusmurskeen määrää lisättäessä. Pölyn muodostumista lisäsivät lisäksi murskeen heikompi iskunkestävyys sekä materiaalin korkea hienoainespitoisuus. Rengastyyppien vertailu koeolosuhteissa osoitti, että pölyä muodostui enemmän nastoitetuilla talvirenkailla kuin nastoittamattomilla, nk. kitkarenkailla. Sama trendi havaittiin myös hiekoitetuissa testeissä. Koeolosuhteissa havaitut hiukkaset olivat pääasiassa mineraaleja ja peräisin päällysteen kiviaineksesta ja/tai hiekoitusmurskeesta. Hiekoitetuissa kokeissa havaittiin nk. hiekkapaperi-ilmiö: hiekoitusmurske hajoaa pienemmiksi partikkeleiksi renkaan alla, mutta syntyneet partikkelit kuluttavat myös päällystettä. Näin ollen pölyssä havaitaan hiukkasia molemmista lähteistä. Myös kaupunkiolosuhteissa talvihiekoituksen havaittiin olevan tärkeä hiukkaslähde. Hiukkasten massakokojakaumassa karkeat hiukkaset muodostavat valtaosan, mutta myös pienhiukkasia (PM2.5) havaittiin.
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