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Elucidating the changing particulate matter pollution and associated health effects in rural India during 2000–2019

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... The trend analysis performed by Chetna et al. (2023) on air quality data for Delhi for the period 2007 to 2021 failed to obtain a significant trend for the post-monsoon period, which may be due to the averaging of data for this period. However, Pathak and Kuttippurath (2024) reported that rural areas in the Indo-Gangetic Plain (IGP) showed a significant rising trend (+ 2.6 µg/m 3 per year) in PM 2.5 during winter. Urban expansion, deforestation, and increased industrial activities alter the natural landscape, leading to higher emissions of pollutants (Romero et al., 1999). ...
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Air pollution is a global issue that demands urgent attention due to its detrimental effects on human health and the environment. Land Use and Land Cover (LULC) change is an essential factor that significantly impacts ambient air quality through alterations in emission sources, vegetation cover, natural processes, and urban design. This study investigates the spatio-temporal variation of key air pollutants resulting from urban LULC changes in the Delhi region. Findings reveal a notable increase in pollutant concentrations, particularly particulate matter, in 2019 (PM10: 318.65 ± 45.80 µg/m³) and 2023 (PM10: 383.70 ± 61.49 µg/m³), compared to 2008 (PM10: 246.76 ± 30.66). LULC change analysis demonstrates a rise in built-up areas 24.59%(2008 to 2019), 33.62% (2008 to 2023) and a decline in vegetation cover 27.49% (2008 to 2019),32.37% (2008 to 2023). Correlation analysis indicates a positive correlation between PM10 and urban indices (+ 0.63) and a negative correlation between PM10 and vegetation indices (− 0.61), highlighting the impact of LULC on air quality deterioration. Subsequently, a fuzzy inference system model integrates LULC information to develop an air quality index (AQI). Incorporating LULC changes in AQI assessment offers a realistic approach to address the complexity arising from combined air pollutant effects, surpassing conventional AQI calculation methods. The findings underscore the significance of understanding the impact of Land Use and Land Cover (LULC) change on ambient air quality in formulating effective air quality management programs and policies. Integrating this knowledge into policymaking is crucial for the successful abatement of air pollution in urbanized areas.
... Chetna et al. (2022) found a significant decrease in PM 2.5 levels over Delhi from 2007 to 2021, with a notable decline during summer (− 3.05 µg m −3 year −1 ; p < 0.1), an insignificant declining trend of − 1.95 µg m −3 year −1 for monsoon, and no significant trends in post-monsoon and winter seasons. Pathak & Kuttippurath (2024) reported that rural IGP shows a significant rising trend of + 2.6 µg m −3 year −1 in PM 2.5 during winter. ...
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The paper presents an estimate of emission source influence on PM10 concentrations in Berlin. Particulate matter less than 10 mum in aerodynamic diameter (PM10) is a conglomerate of different chemical components related to distinct sources and physico-chemical processes in the atmosphere and lithosphere. Emission reduction thus has temporally and spatially varying effects on different scales. Urban PM10 concentrations are heavily influenced by long range transport (up to 70%) from remote source areas, whereas rural air pollution is strongly determined by urban emissions. By means of emission reduction scenario simulations with a chemistry-transport-model it has been found that on average two third of the urban background concentrations in Berlin are due to Berlin-specific emissions. This percentage varies strongly considering primary and secondary components: only about 5% of secondary PM10 concentrations are related to local emissions, while approximately 70% of primary concentrations stem from the urban sources. City related emissions influence homogenously the rural air-pollution concentrations, but with different ranges of influence.
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The long term climatology of PM10 and PM2.5 concentrations for the five year period from June 2007-March 2012 is studied using measurements made with a Quartz Crystal Microbalance Impactor over Dibrugarh, North-East India. The PM10 and PM2.5 exhibit similar seasonal variability with maximum concentration in winter and minimum in monsoon seasons. The PM10 concentration is mainly attributed to PM2.5 with minimal contribution from PM10-2.5. The long term monthly mean PM10 and PM2.5 concentrations shows maximum value in late winter and early pre-monsoon. This temporal variability is positively correlated with the MODIS retrieved fire counts associated mostly with the biomass burning activities and negatively correlated with rainfall. PM10 and PM2.5 gradually increased from 2007 to 2010 and decreased thereafter. An overall slow decreasing trend in PM10 and PM2.5 concentrations together with black carbon (BC) concentrations has been observed. The examination of microphysical and optical properties also reveals the dominance of PM2.5 aerosols. Higher percentage contributions of BC to both PM10 and PM2.5 are observed in post-monsoon season followed by winter. The inter-comparison of measured PM and BC concentrations with SPRINTARS simulation reveals that model underestimates the measurements except in pre-monsoon. The discrepancy might have arisen due to the topography of the location and inadequate emission inventory for the climate zone.
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Using aerosol optical depth as a function of wavelength obtained from ground-based multiwavelength radiometer observations, columnar size-distribution functions of aerosols have been derived. It has been found that the nature of the derived size-distribution function is strongly dependent on season. The derived size-distribution functions are discussed in terms of seasonally dependent natural aerosol sources and sinks.
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The U.S. Environmental Protection Agency (EPA) and the federal land management community (National Park Service, United States Fish and Wildlife Service, United States Forest Service, and Bureau of Land Management) operate extensive particle speciation monitoring networks that are similar in design but are operated for different objectives. Compliance (mass only) monitoring is also carried out using federal reference method (FRM) criteria at approximately 1000 sites. The Chemical Speciation Network (CSN) consists of approximately 50 long-term-trend sites, with about another 250 sites that have been or are currently operated by state and local agencies. The sites are located in urban or suburban settings. The Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring network consists of about 181 sites, approximately 170 of which are in nonurban areas. Each monitoring approach has its own inherent monitoring limitations and biases. Determination of gravimetric mass has both negative and positive artifacts. Ammonium nitrate and other semivolatiles are lost during sampling, whereas, on the other hand, measured mass includes particle-bound water. Furthermore, some species may react with atmospheric gases, further increasing the positive mass artifact. Estimating aerosol species concentrations requires assumptions concerning the chemical form of various molecular compounds, such as nitrates and sulfates, and organic material and soil composition. Comparing data collected in the various monitoring networks allows for assessing uncertainties and biases associated with both negative and positive artifacts of gravimetric mass determinations, assumptions of chemical composition, and biases between different sampler technologies. All these biases are shown to have systematic seasonal characteristics. Unaccounted-for particle-bound water tends to be higher in the summer, as does nitrate volatilization. The ratio of particle organic mass divided by organic carbon mass (Roc) is higher during summer and lower during the winter seasons in both CSN and IMPROVE networks, and Roc is lower in urban than non-urban environments.
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This work provides long-term (2004–2006) size segregated measurements of aerosol mass at a remote coastal station in the southern Europe, with the use of size-selective samplings (SDI impactor). Seven distinct modes were identified in the range 0–10 µm and the dominant were the "Accumulation 1" (0.25–0.55 µm) and the "Coarse 2" (3–7 µm) modes. The seasonal characteristics of each mode were thoroughly studied and different sources for submicron and supermicron particles were identified, the first being related to local/regional and transported pollution with maximum in summer and the latter to dust from deserted areas in Northern Africa maximizing in spring. On average, PM2.5 and PM1 accounted for 60% and 40% of PM10 mass, respectively.The representativity of the ground-based measurements for the total column was also investigated by comparing the measured aerosol mass distributions with the AERONET volume size distribution data. Similar seasonal patterns were revealed and AERONET was found adequate for the estimation of background levels of both fine and coarse particles near surface, with certain limitations in the case of pollution or dust abrupt episodes due to its low temporal coverage.
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There is a significant increase in ozone at the surface and troposphere due to growing population, industrialization and urbanization. The initiation of National Clean Air Programme (NCAP) in 2019 marked a turning point in addressing air pollution in Indian cities. The Central Pollution Control Board (CPCB) ground-based measurements show a reduction in number of days with continuous exposure to 8 h surface ozone (MDA-8) exceeding 100 ppb since the implementation of NCAP. For instance, cities such as Visakhapatnam and Tirupati reported zero days of MDA-8 ozone surpassing 100 ppb in 2022. Also, a substantial reduction is observed in the frequency of MDA-8 ozone exceeding the 100 ppb threshold at other stations. The NO2 and PM2.5 measurements from CPCB show a decreasing trend at most stations, whereas satellite-based HCHO and NO2 measurements show negative (0–0.004 mol m−2 month−1) and positive (0–0.02 m−2 month−1) trends, respectively, during the period of 2019–2022. Therefore, although the implementation of NCAP is oriented towards reducing PM10 concentrations, it is also proven to be effective in curbing ozone pollution in most cities of India. This study, therefore, suggests to continue the efforts of NCAP and to implement tailored regulations for reducing ozone pollution in cities with high pollution.
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A year-long study (January-December 2019) on the chemical characterization and meteorological impact on PM2.5 was conducted over a semi-urban station, Shyamnagar, in the easternmost part of the Indo-Gangetic Plains (IGP). PM2.5 concentrations (Mean = 81.69 ± 66.27 μgm-3; 7.10-272.74 μgm-3), the total carbonaceous aerosols (TCA) (Mean = 22.85 ± 24.95 μgm-3; 0.77-102.97 μgm-3) along with differential carbonaceous components like organic carbon (OC) (Mean = 11.28 ± 12.48 μgm-3; 0.48-53.01 μgm-3) and elemental carbon (EC) (Mean = 4.83 ± 5.28 μgm-3; 0.1-22.13 μgm-3) exhibited prominent seasonal variability with the highest concentrations during winter, followed by post-monsoon, pre-monsoon and lowest during monsoon. A similar seasonal variation was observed for the total water-soluble ionic species (Mean = 31.91 ± 20.12 μgm-3; 0.1-126.73 μgm-3). We observed that under the least favorable conditions (low ventilation coefficient), high PM2.5 pollution (exceeding Indian standard) was associated with a high increase in secondary components of PM2.5. Eastern, central and western parts of IGP, as well as Nepal, were the major long-distant source regions whereas the northern part of West Bengal and parts of Bangladesh were the major regional source region for high PM2.5 pollution over Shyamnagar. The ratios like char-EC/soot-EC, non-sea-K+/EC and non-sea-SO42-/EC strongly indicated the dominance of fossil fuel burning over biomass burning. Compared with other studies, we observed that the PM2.5 pollution over this semi-urban region was comparable (and even higher in some cases) with other parts of IGP. The high exceedance of PM2.5 over the Indian standard in Shyamnagar strongly demands an immediate initiation of systematic and regular based air pollution monitoring over semi-urban/non-urban regions in India, especially IGP, in addition to the polluted cities.
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It is highly valuable to obtain high-quality PM2.5 concentration worldwide for continuous monitoring of global air pollution. Recently, global reanalysis products of PM2.5 have come into the view. However, most studies focus on the validation and calibration of a single product regionally, few studies expand to a global scale and integrate multiple products. With the help of global open-source data provided by the OpenAQ platform, we propose a hybrid calibration method aimed to improve the accuracy of CAMSRA and the MERRA-2 PM2.5 products. In the study, the accuracy of the two datasets are assessed on multi-time scales at first. Secondly, we try to use some machine learning models to correct the deviation of the original products alone and then further explore the possibility of the hybrid calibration. Global-scale validation results show that CAMSRA products are generally overestimated (daily R = 0.6), and MERRA-2 products are underestimated (daily R = 0.3), which supports our hybrid calibration method to an extent. Using the Extremely Randomized Tree (ERT) to implement the separate calibration scheme, two products show different degrees of accuracy improvement, to be specific, R increases by 0.19 and 0.43 for daily CAMSRA and MERRA-2 products, respectively. Compared with the separate calibration modeling, the hybrid method performs better, with R reaching up to 0.81. RMSE is only 14.94 μg/m³, which has a decrease of 60.99% and 64.42% to two abovementioned original products. The obtained daily PM2.5 maps have higher quality with no data gaps, which can be a promising data source of air pollution monitoring and health research. This dataset is published in GeoTIFF format at https://doi.org/10.5281/zenodo.5168102.
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Atmospheric aerosols play key roles in radiation budget, ecosystem dynamics, air quality and cloud microphysics of a region and thus, they greatly influence the global climate, ecosystem and public health. We present the temporal variability of atmospheric aerosols over India and north Indian Ocean (NIO) for the past two decades (2000–2019). Here, the measurements from Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra, Multi-angle Imaging Spectroradiometer (MISR) on Terra, and Advanced Along Track Scanning Radiometer (AATSR) measurements from Envisat, and Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis data are considered. On average, the Indo Gangetic Plain (IGP) shows the largest (0.5–1.2), and NIO and Himalaya show the smallest (0.1 or smaller) Aerosol Optical depth (AOD) values. The peak AOD loading is observed in June–July-August, and IGP has a secondary peak in March owing to the stubble and biomass burning in winter months. In contrast, the peak aerosol loading in the northeast is in winter due to forest and biomass burning during the period there. The inter-annual variability is very small in NIO, Himalaya and Bay of Bengal. The trends estimated from the combined (AATSR, MISR, MODIS and MERRA-2) data show the highest positive trends at the lower IGP and east central regions, about 0.8–1.2/dec, and are statistically significant. This is consistent with the urban activity, industries and dense population there. However, the Desert, northern Himalaya and northern Arabian Sea show insignificant negative trends, from −0.2 to −0.4/dec, as the anthropogenic sources of aerosols are very limited there. The bias estimate shows that most satellite and reanalysis data are in very good agreement at all regions (within 0.1–0.2). Even though the bias in the measurements are considered, the trends estimated are still large enough to be statistically significant. The analyses, therefore, caution the increasing aerosol loading and their plausible climate feedback in these regions. The assessment also demonstrate the potential of synergetic use of multiple-platform measurements for climate system studies.
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In recent years, the frequent occurrence of haze events in the Indo-Gangetic Plain (IGP) during crop residue burning period has caused a serious reduction in atmospheric visibility and deteriorated air quality. The present study is carried out to investigate the haze event observed in IGP in Nov 2017 using ground-based observations, satellite data and synoptic meteorology to understand the possible factors responsible for haze formation. PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 µm) concentrations and Air Quality Index (AQI) at two sites (Agra and Delhi) situated in the central Indo-Gangetic Plain (CIGP) showed a sudden increase in PM2.5 concentrations and deteriorated air quality during 7-14 Nov. To monitor the variation of particulate matter (PM) in IGP, PM2.5 and PM10 (particulate matter with aerodynamic diameter ≤ 10 µm) concentrations were monitored at 22 stations in 12 cities of IGP during 1-15 Nov which also showed an increase in PM concentrations during haze event (7-14 Nov). Crop residue burning activities in north-west Indo-Gangetic Plain (NW-IGP) were observed during haze event. Synoptic weather conditions of IGP identified using geopotential height and wind at 700 hPa showed high-pressure systems and low winds in IGP favoring stagnant conditions during haze event. A detailed analysis of the variation of pollutants and meteorology was carried out at Agra. Ozone (O3), carbon monoxide (CO), sulphur dioxide (SO2) and nitrogen oxides (NOx) showed higher concentrations during haze event along with lower temperature, low wind speed and high relative humidity. Aerosol ionic composition showed a higher contribution (~84%) of Cl-, NO3-, SO42- and NH4+ to total soluble ions suggesting secondary aerosol formation during haze event.
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Air pollution affects not only the air quality in megacities but also in medium and small-sized cities due to rapid urbanization, industrialization, and other anthropogenic activities. From October 28, 2015 to November 3, 2015, the Indo-Gangetic Plains region, including Chandigarh encountered an episode of poor visibility during the daytime. The daily average PM2.5 concentration reached 191 μg/m3, and visibility reduced by ∼2.2 times in the Chandigarh region. PM2.5 concentration was found around 4 times higher than a non-haze day and more than 3 times higher than National Ambient Air Quality Standards for 24 h. A significant correlation between PM2.5 and CO (r: 0.87) during the haze period indicated similarity in their emission sources; which was attributed to the burning of solid organic matter. Further, satellite data and back-trajectory analysis of air masses showed large-scale rice stubble burning in the agricultural fields, adjoining to the city areas. The transboundary movement of air masses below 500 m and meteorological conditions played a major role in building the pollution load in the Chandigarh region. Moreover, the enhanced concentration of biomass burning tracers, i.e., organic carbon (∼3.8 times) and K⁺ ions (2∼ times) in PM2.5 and acetonitrile (∼2.3 times) in ambient air was observed during the haze event. The study demonstrates how regional emissions and meteorological conditions can affect the air quality in a city; which can be useful for proper planning and mitigation policies to minimize high air pollution episodes.
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Rising levels of air-borne suspended particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5) in the Asian mega cities is a serious cause of concern. A severe SMOG (smoke+fog) episode with air-visibility of about 5 km or shorter and average PM2.5 concentration of 793μgm−3 occurred in Delhi and the National Capital Territory (NCT) just after the Diwali-festival and spanned between 30 October (Oct.) and 07 November (Nov.) 2016. Using daily variations in chemical and isotopic signatures of PM2.5 in tandem with meteorological parameters, we deduce here primary contributing factors responsible for development of Delhi-SMOG-2016 event and dominant transformation pathways of carbon, sulphur, and nitrogen compounds. Our multi-tracer analyses revealed there could be three major factors contributing to the SMOG event: (i) transport of carbo- naceous material from North-Western India (mainly Punjab-Haryana) due to open-field agricultural-waste burning that rose from 26 Oct. through 11 Nov. 2016, (ii) weaker northerly winds, shallower boundary layer, cooler air temperatures, and enhanced humidity enforcing ‘atypical’ air-stagnation, and (iii) direct emissions from fire-cracker bursting on 30 Oct. 2016 (Diwali night). Investigating timing of agricultural waste burning in northwester states of Delhi, there appears to be a delay in the timing of agricultural-waste burning in the Punjab area since 2010; that could possibly causing a gradual increase in severity of SMOG events in the recent years.
Article
North China Plain (NCP) is one of heavily polluted regions that is characterized by a mixture of a myriad of anthropogenic and natural aerosols. A substantial spatial and temporal variations of aerosols and their compositions there poses a good testbed for the validation of model simulations. Aerosol optical depth (AOD) and PM2.5 (particulate matter with aerodynamic diameter < 2.5 μm) concentration products from the Modern Era Retrospective-Analysis for Research and Applications, version 2 (MERRA-2) are evaluated using available independent ground-based in situ and remote sensing products in the NCP. The comparison of MERRA-2 aerosol species to the observations is also performed. Although several satellite and ground-based AOD products are assimilated into the MERRA-2, MERRA-2 AOD is systematically smaller than independent sunphotometer measurements. The biases range from 0.09 (13%) in the summer to 0.17 (33%) in the spring and show little spatial dependence. Daytime AOD variations are captured by the MERRA-2, although MERRA-2 has relatively lower AOD. MERRA-2 produces lower PM2.5 concentration relative to surface measurements in all seasons except in summer. The largest bias is found in the winter (44 μgm⁻³). On the contrary, summer MERRA-2 PM2.5 is close to surface-measured PM2.5 (with bias of 0.4 μgm⁻³). MERRA-2 was unable to reproduce diurnal PM2.5 variation. Evaluation of MERRA-2 aerosol species in the winter of 2014 suggests that MERRA-2 could not keep track of dramatic day-to-day variation of aerosols and their species. Potential causes for this deficiency may include a lack of nitrate aerosols (accounting for 20% of PM2.5 concentrations during heavily polluted days). This fault cannot be remedied by assimilation of satellite AODs because they are often missing.
Article
Lack of a consistent PM10 (particulate matter smaller than 10 µm) database at high spatial resolution hinders in assessing the environmental impact of PM10 in India. Here we propose an alternate approach to estimate PM10 database. Aerosol extinction coefficients at the surface are calculated from mid-visible aerosol optical depth from MERRA-2 reanalysis data using characteristics vertical profiles from CALIOP and then are converted to PM10 mass using aerosol property information and microphysical data. The retrieved PM10 are bias-corrected and evaluated (R2=0.85) against coincident ground-based data maintained under Central Pollution Control Board network. PM10 exposure exceeds Indian annual air quality standard in 72.3% districts. Transition in PM10 exposure from the monsoon (Jun-Sep) to post-monsoon season (Oct-Nov) translates to 1-2% higher all-cause mortality risk over the polluted Indo-Gangetic Basin (IGB). Mortality risk increases in the central to eastern IGB and central India and reduces in Delhi national capital region in the winter (Dec-Feb) relative to the post-monsoon season. Mortality risk decreases by 0.5-1.8% in most parts of India in the pre-monsoon season (Mar-May). Our results quantify the vulnerability in terms of seasonal transition in all-cause mortality risks due to PM10 exposure at district level for the first time in India.
Article
The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), is NASA’s latest reanalysis for the satellite era (1980 onward) using the Goddard Earth Observing System, version 5 (GEOS-5), Earth system model. MERRA-2 provides several improvements over its predecessor (MERRA-1), including aerosol assimilation for the entire period. MERRA-2 assimilates bias-corrected aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer and the Advanced Very High Resolution Radiometer instruments. Additionally, MERRA-2 assimilates (non bias corrected) AOD from the Multiangle Imaging SpectroRadiometer over bright surfaces and AOD from Aerosol Robotic Network sunphotometer stations. This paper, the second of a pair, summarizes the efforts to assess the quality of the MERRA-2 aerosol products. First, MERRA-2 aerosols are evaluated using independent observations. It is shown that the MERRA-2 absorption aerosol optical depth (AAOD) and ultraviolet aerosol index (AI) compare well with Ozone Monitoring Instrument observations. Next, aerosol vertical structure and surface fine particulate matter (PM2.5) are evaluated using available satellite, aircraft, and ground-based observa- tions. While MERRA-2 generally compares well to these observations, the assimilation cannot correct for all deficiencies in the model (e.g., missing emissions). Such deficiencies can explain many of the biases with observations. Finally, a focus is placed on several major aerosol events to illustrate successes and weaknesses of the AOD assimilation: the Mount Pinatubo eruption, a Saharan dust transport episode, the California Rim Fire, and an extreme pollution event over China. The article concludes with a summary that points to best practices for using the MERRA-2 aerosol reanalysis in future studies.
Article
Seasonal variation of PM2.5 (Particulate Matter <2.5 μm) mass concentration simulated from WRF-Chem (Weather Research and Forecasting coupled with Chemistry) over Indian sub-continent are studied. The simulated PM2.5 are also compared with the observations during winter, pre-monsoon, monsoon and post-monsoon seasons of 2008. Higher value of simulated PM2.5 is observed during winter followed by post-monsoon, while lower values are found during monsoon. Indo-Gangetic Basin (IGB) exhibits high amount of PM2.5 (60- 200 μg m(-3)) throughout the year. The percentage differences between model simulated and observed PM2.5 are found higher (40- 60%) during winter, while lower (< 30%) during pre-monsoon and monsoon over most of the study locations. The weighted correlation coefficient between model simulated and observed PM2.5 is 0.81 at the significance of 98%. Associated RMSE (Root Mean Square Error) is 0.91 μg m(-3). Large variability in vertically distributed PM2.5 are also found during pre-monsoon and monsoon. The study reveals that, model is able to capture the variabilities in spatial, seasonal and vertical distributions of PM2.5 over Indian region, however significant bias is observed in the model. PM2.5 mass concentrations are highest over West Bengal (82± 33 μg m(-3)) and the lowest in Jammu & Kashmir (14± 11 μg m(-3)). Annual mean of simulated PM2.5 mass over the Indian region is found to be 35± 9 μg m(-3). Higher values of PM2.5 are found over the states, where the reported respiratory disorders are high. WRF-Chem simulated PM2.5 mass concentration gives a clear perspective of seasonal and spatial distribution of fine aerosols over the Indian region. The outcomes of the study have significant impacts on environment, human health and climate.
Article
Abstract Size-segregated particulate pollutants (PM<0.95, PM0.95–1.5, PM1.5–3.0, PM3.0–7.2 and PM>7.2) were collected over Patiala (30.33°N, 76.40°E; 250 m amsl), a semi-urban city located in northwestern Indo-Gangetic Plain (IGP), during October, 2012 to September, 2013. Mass concentration of total suspended particulates (TSP), derived by summation of particulate (aerosol) mass in different size range, varied from 88 to 387 μg m−3 with highest mass concentration (∼55% of total mass) in submicron size (PM<0.95) during the entire study period, which broadly reflects relative higher contribution of various anthropogenic sources (emissions from biomass and bio-fuel burning, vehicles, thermal power plants, etc) to ambient particles. Concentration of SO42−, NO3−, NH4+, K+ and Ca2+ exhibited large variability ranging from 0.52 to 40, 0.20 to 19, 0.14 to 12, 0.06 to 5.3 and 0.08 to 5.6 μg m−3, respectively, in different size ranges with varying size distribution for most of the species, except NH4+. A strong linear correlation (r = 0.97) between (SO42− + NO3−) and (K+ + NH4+) concentrations has been observed in submicron particles collected in different seasons, suggesting the formation of secondary inorganic salts. However, relatively poor correlation is observed in higher size ranges where significant correlation between (SO42− + NO3−) and (Ca2+ + Mg2+) has been observed. These observations indicate the acid neutralization by dust in coarser modes of particles. Chemical composition of submicron particulates (PM<0.95) in different seasons as well as for whole year was used to identify PM sources through the application of Positive Matrix Factorization (PMF, version 5.0) model. Based on annual data, PMF analyses suggests that six source factors namely biomass burning emission (24%), vehicular emission (22%), secondary organic aerosols (20%), power plant emission (13%), secondary inorganic aerosols (12%) and mineral dust (9%) contribute to PM<0.95 loading over the study region. Such studies are important in dispersion modeling, health impact assessment, and planning of pollution mitigation strategies.
Article
India's population is exposed to dangerously high levels of air pollution. Using a combination of ground-level in situ measurements and satellite-based remote sensing data, this paper estimates that 660 million people, over half of India's population, live in areas that exceed the Indian National Ambient Air Quality Standard for fine particulate pollution. Reducing pollution in these areas to achieve the standard would, we estimate, increase life expectancy for these Indians by 3.2 years on average for a total of 2.1 billion life years. We outline directions for environmental policy to start achieving these gains.
Article
HYSPLIT, developed by NOAA’s Air Resources Laboratory, is one of the most widely used models for atmospheric trajectory and dispersion calculations. We present the model’s historical evolution over the last 30 years from simple hand drawn back trajectories to very sophisticated computations of transport, mixing, chemical transformation, and deposition of pollutants and hazardous materials. We highlight recent applications of the HYSPLIT modeling system, including the simulation of atmospheric tracer release experiments, radionuclides, smoke originated from wild fires, volcanic ash, mercury, and wind-blown dust.
Article
Black carbon (BC) aerosol mass concentrations measured using an aethalometer at Ahmedabad, an urban location in western India, from September 2003 to June 2005 are analyzed. BC mass concentrations are found to show diurnal and seasonal variations. Diurnal evolution in BC is marked with two peaks, one in the morning hours, just after the sunrise, and the other in the late evening hours. The peaks occur due to fumigation effect of boundary layer, gradual increase in the anthropogenic activities, and rush hour traffic. January BC values are about a factor of 5 higher than July mass concentrations. During winter the surface boundary layer is shallow resulting in trapping of pollutants in a lesser volume which leads to higher BC concentrations. In July an increase in boundary layer height, surface temperature, convective activity, and rainfall result in lower BC values. BC mass concentrations are about 0.8 μg m−3 in July 2004 (southwest monsoon), while BC was higher than 5 μg m−3 in January 2004 (northeast monsoon). Ahmedabad BC mass concentrations are higher than those measured over central, western India and Hyderabad, an urban city in south India during a land campaign in February 2004. BC values measured over Ahmedabad are found to be higher than those measured over various locations representing different environments in Europe. Seasonal variations are less pronounced in urban locations in Europe. BC mass concentrations at east St. Louis, Illinois, an urban site are found to be less than 2 μg m−3 during September 2003 to June 2005, with less pronounced seasonal variations. BC mass concentrations at various land locations in India, Beijing, and Seoul are higher than those measured over various locations in Europe, Canada, and the United States.
Article
We present the first detailed analysis of a 9 year (2000–2008) seasonal climatology of size- and shape-segregated aerosol optical depth (AOD) and Ångström exponent (AE) over the Indian subcontinent derived from the Multiangle Imaging Spectroradiometer (MISR). Our analysis is evaluated against in situ observations to better understand the error characteristics of and to corroborate much of the space-time variability found within the MISR aerosol properties. The space-time variability is discussed in terms of aerosol sources, meteorology, and topography. We introduce indices based on aerosol size- and shape-segregated optical depth and their effect on AE that describe the relative seasonal change in anthropogenic and natural aerosols from the preceding season. Examples of major new findings include the following: (1) winter to premonsoon changes in aerosol properties are not just dominated by an increase in dust, as previously thought, but also by an increase in anthropogenic components, particularly in regions where biomass combustion is prevalent; (2) ∼15% of the AOD over the high wintertime pollution in the eastern Indo-Gangetic basin is due to large dust particles, resulting in the lowest AE (
Article
Despite improvements in vehicle and fuel technology that have led to reductions in primary particle emissions, high PM10 levels have been observed in recent years in several European cities, including Athens (Greece) and Birmingham (UK). In certain cases, high PM10 concentrations have persisted over periods of several hours, resulting in exceedences of EU target values. In order to design effective PM10 control strategies, it is essential to develop an understanding of local and remote sources of particulate matter, as well as of the factors influencing its temporal and spatial variability in urban areas. In this study, PM10 data from Athens and Birmingham were analysed for relationships to other pollutants (NOx, CO, O3 and SO2) and meteorological parameters (wind velocity, temperature, relative humidity, precipitation, solar radiation and atmospheric pressure) during a 3-year period (2001–2003). Significant positive correlations between PM10 and NOx, CO, and solar radiation were observed at the selected monitoring sites during cold seasons. On the other hand, negative correlations between PM10 and O3, wind speed and precipitation were observed during the same seasons. However, these correlations became weaker during warm seasons, probably due to secondary aerosol formation and enhanced soil dust re-suspension. Furthermore, principal component and regression analyses were used to quantify the contribution of non-combustion sources to the observed PM10 background levels. This contribution ranged between 45% and 70% in Birmingham and 41–74% in Athens. Finally, several winter and summer PM10 episodes from each city were analysed using a back trajectory model, in order to identify the origin of the polluted air masses. It was found that long-range transport of particles from continental Europe had a marked effect on PM10 background levels in Birmingham, while the local weather had a stronger influence on PM10 levels in Athens.
Article
The background aerosol in the boundary layer over the remote oceans is not aged continental aerosol but, rather, is largely of marine origin. Total particle concentrations are quite uniform throughout the tropical trade wind regions and normally are in the range of 100–300 cm⁻³. Precipitation reduces particle concentrations, but there is apparently an in situ source of small particles which allows particle concentrations to recover to their normal background level. The fine particle mode (r < 0.3 μm), which comprises 90–95% of the particles but only about 5% of the total mass, cosists primarily of non-sea-salt sulfate (nss-sulfate). There is considerable evidence that nss-sulfate, which is present in concentrations ranging from about 0.2 to 1.5 μg m⁻³, is formed by gas-to-particle conversion of the oxidation products of organosulfur gases (principally DMS) emitted by the ocean. The principal gas-to-particle conversion mechanisms are particle formation by homogeneous nucleation of low-volatility gas-phase reaction products, condensation of these products on existing particles, and SO2-to-sulfate conversion in cloud droplets. The submicron portion of the particle size distribution is bimodal with peaks at 0.03 μm and 0.1 μm radius. The peak at 0.1 μm is believed to be due to the growth of CCN-sized particles as a result of incloud SO2-to-sulfate conversion. It has been speculated that the sea-to-air flux of DMS affects the number of CCN and thereby affects cloud droplet size, cloud albedo and, consequently, climate.
Article
A comprehensive, spatially resolved (0.25°×0.25°) fossil fuel consumption database and emissions inventory was constructed, for India, for the first time. Emissions of sulphur dioxide and aerosol chemical constituents were estimated for 1996–1997 and extrapolated to the Indian Ocean Experiment (INDOEX) study period (1998–1999). District level consumption of coal/lignite, petroleum and natural gas in power plants, industrial, transportation and domestic sectors was 9411 PJ, with major contributions from coal (54%) followed by diesel (18%). Emission factors for various pollutants were derived using India specific fuel characteristics and information on combustion/air pollution control technologies for the power and industrial sectors. Domestic and transportation emission factors, appropriate for Indian source characteristics, were compiled from literature. SO2 emissions from fossil fuel combustion for 1996–1997 were 4.0 Tg SO2 yr−1, with 756 large point sources (e.g. utilities, iron and steel, fertilisers, cement, refineries and petrochemicals and non-ferrous metals), accounting for 62%. PM2.5 emitted was 0.5 and 2.0 Tg yr−1 for the 100% and the 50% control scenario, respectively, applied to coal burning in the power and industrial sectors. Coal combustion was the major source of PM2.5 (92%) primarily consisting of fly ash, accounting for 98% of the “inorganic fraction” emissions (difference between PM2.5 and black carbon+organic matter) of 1.6 Tg yr−1. Black carbon emissions were estimated at 0.1 Tg yr−1, with 58% from diesel transport, and organic matter emissions at 0.3 Tg yr−1, with 48% from brick-kilns. Fossil fuel consumption and emissions peaked at the large point industrial sources and 22 cities, with elevated area fluxes in northern and western India. The spatial resolution of this inventory makes it suitable for regional-scale aerosol-climate studies. These results are compared to previous studies and differences discussed. Measurements of emission factors for Indian sources are needed to further refine these estimates.
Article
Aerosols are of central importance for atmospheric chemistry and physics, the biosphere, climate, and public health. The airborne solid and liquid particles in the nanometer to micrometer size range influence the energy balance of the Earth, the hydrological cycle, atmospheric circulation, and the abundance of greenhouse and reactive trace gases. Moreover, they play important roles in the reproduction of biological organisms and can cause or enhance diseases. The primary parameters that determine the environmental and health effects of aerosol particles are their concentration, size, structure, and chemical composition. These parameters, however, are spatially and temporally highly variable. The quantification and identification of biological particles and carbonaceous components of fine particulate matter in the air (organic compounds and black or elemental carbon, respectively) represent demanding analytical challenges. This Review outlines the current state of knowledge, major open questions, and research perspectives on the properties and interactions of atmospheric aerosols and their effects on climate and human health.