Article

Detection of PM2.5 plume movement from IoT ground level monitoring data

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Abstract

In this study, we analysed a data set from 10 low-cost PM2.5 sensors using the Internet of Things (IoT) for air quality monitoring in Mae Sot, which is one of the most vulnerable areas for high PM2.5 concentration in Thailand, during the 2018 burning season. Our objectives were to understand the nature of the plume movement and to investigate possibilities of adopting IoT sensors for near real-time forecasting of PM2.5 concentrations. Sensor data including PM2.5 and meteorological parameters (wind speed and direction) were collected online every 2 min where data were grouped into four zones and averaged every 15 min interval. Results of diurnal profile plot revealed that PM2.5 concentrations were high around early to late morning (3:00-9:00) and gradually reduced till the rest of the day. During the biomass burning period, maximum daily average concentration recorded by the sensors was 280 μg/m3 at Thai Samakkhi while the minimum was 13 μg/m3 at Mae Sot. Lag time concentrations, attributed by biomass burning (hotspots), significantly influenced the formation of PM2.5 while the disappearance of PM2.5 was found to be influenced by moderate wind speed. The PM2.5 concentrations of the next 15 min at the downwind zone (MG) were predicted using lag time concentrations with different wind categories. The next 15 min predictions of PM2.5 at MG were found to be mainly influenced by its lag time concentrations (MG_Lag); with higher wind speed, however, the lag time concentrations from the upwind zones (MS_Lag and TS_Lag) started to show more influence. From this study, we have found that low-cost IoT sensors provide not only real-time monitoring information but also demonstrate great potential as an effective tool to understand the PM2.5 plume movement with temporal variation and geo-specific location.

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... Particularly during dry seasons, biomass and agricultural burning significantly impact the air quality in SEA, with high PM2.5 concentrations usually affected by regional wind directions, high temperatures, low rainfall, and phenomena such as the El Niño-Southern Oscillation (ENSO) [30,31]. Field campaigns for biomass and agriculture burning using LCPMS in Asia have been conducted, indicating high PM2.5 concentrations in Hanoi [32,33], the northern region of Thailand [34], and New Delhi [35] during biomass burning episodes; sometimes the PM2.5 levels exceeded 100 μg/m 3 . The PM2.5 concentrations were influenced by the number of hot spots and wind direction toward the sampling stations. ...
... The average PM2.5 concentration was 19.1 μg/m 3 in the Petaling Jaya area near Kuala Lumpur using AiRBOXSense, as shown in Table 2 [79]. Compared to those measurements during biomass burning with more than 100 μg/m 3 [32][33][34][35], this demonstrated the significant impacts of the transboundary transport of biomass burning on ambient PM2.5 levels in Malaysia during burning seasons. Therefore, the multiple deployments of LCPMS in downwind locations can be used to assess the biomass burning impacts due to regional transport in the ambient PM2.5 levels in areas without EPA stations in SEA. ...
... There are only two international publications providing information on ambient PM2.5 monitoring results, both in northern Thailand. Ten sensor nodes (Plantower PMS7003) were deployed in the Mae Sot district, Tak province, located at the borderline of Thailand and Myanmar during March-April 2018 to investigate the movement of biomass burning plumes [34]. The results showed that during an intensive biomass burning period, the 24-h PM2.5 reached a maximum of 280 μg/m 3 ( Table 2). ...
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The low-cost and easy-to-use nature of rapidly developed PM2.5 sensors provide an opportunity to bring breakthroughs in PM2.5 research to resource-limited countries in Southeast Asia (SEA). This review provides an evaluation of the currently available literature and identifies research priorities in applying low-cost sensors (LCS) in PM2.5 environmental and health research in SEA. The research priority is an outcome of a series of participatory workshops under the umbrella of the International Global Atmospheric Chemistry Project-Monsoon Asia and Oceania Networking Group (IGAC-MANGO). A literature review and research prioritization are conducted with a transdisciplinary perspective of providing useful scientific evidence in assisting authorities in formulating targeted strategies to reduce severe PM2.5 pollution and health risks in this region. The PM2.5 research gaps that could be filled by LCS application are identified in five categories: source evaluation, especially for the distinctive sources in the SEA countries; hot spot investigation; peak exposure assessment; exposure-health evaluation on acute health impacts; and short-term standards. The affordability of LCS, methodology transferability, international collaboration, and stake-holder engagement are keys to success in such transdisciplinary PM2.5 research. Unique contributions to the international science community and challenges with LCS application in PM2.5 research in SEA are also discussed. Citation: Lung, S.-C.C.; Thi Hien, T.; Cambaliza, M.O.L.; Hlaing, O.M.T.; Oanh, N.T.K.; Latif, M.T.; Lestari, P.; Salam, A.; Lee, S.-Y.; Wang, W.-C.V.; et al. Research Priorities of Applying Low-Cost PM2.5 Sensors in Southeast Asian Countries. Int. J. Environ. Res.
... For example, AI-based models were implemented to model, optimise, predict, and control pollutant issues related to pollutant removal processes [81][82][83] and analysis and forecast of air pollutants [84][85][86][87]. Other digital transformation technologies, such as big data and IoT, also contribute significantly to pollution control and prevention [88][89][90] (p. 5 in [91]). In [92], blockchain technology was utilised to develop an emission trading system, which was an economic incentive to control environmental pollution. ...
... (ii) Indirect effects: numerous research studies assess the indirect effects of digitalisation and networking on [95] Big data, AI Rainfall prediction system based on neural networks using meteorological data Climate change adaptation 13 [97,99] IoT Managing natural disasters Disaster management 14 [82][83][84] AI Optimise the pollutant removal processes Pollution reduction 15 [85][86][87][88] AI Analysis and forecast of air pollutants Predict air pollution 16 [88] Big data a novel approach for measuring urban air pollution Pollution reduction 17 [92] Blockchain Emission trading system Pollution reduction 18 [90] IoT, big data Low-cost IoT devices for monitoring air quality Monitor pollution 19 [91] IoT, AI Air quality evaluation framework using fixed and mobile sensing units Monitor pollution [39] Big data Analytic of real-time data to reduce cost and waste Reduce waste 3 [30][31][32] Big data Analysis of the construction waste Reduce waste 4 [63] Big data Identify illegal waste dumping cases Reduce waste 5 [64] Big data, IoT Manage the waste electrical and electronic equipment Efficient waste management 6 [32,65,66] IoT Efficient food waste management Reduce food waste 7 [33,34,67] IoT Waste management in smart cities Efficient waste collection 8 [76] AI Assisted the waste collection with DSS Efficient waste collection 9 [68,69] AI Assisted the e-waste collection Efficient waste collection 10 [70] AI Classify the waste using the DL algorithm Efficient waste recycling 11 [72][73][74][75] Blockchain Blockchain technology in managing waste Efficient waste management the environment and support the notion that they could contribute to sustainable development. For example, digital technology such as AI, big data, IoT, and blockchain is revolutionising our approach to biodiversity conservation, clean energy development, and management of natural disasters. ...
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Recently, digital transformation is supposed to affect all aspects of human life profoundly. Nevertheless, there is a lack of summaries map digital transformation in the environmental sustainability domain. To address this knowledge gap, this study examines the impacts of digital transformation on environmental sustainability, including both positive and negative effects. Furthermore, the results highlight the transformations that preserve the environment in three main areas: waste management and handling, pollution prevention and control, and sustainable resource management. Based on the literature summary, this study also discusses the opportunities and challenges in this field, which attempts to offer a vision for further research.
... A thorough review of the selected articles revealed that digital technologies, including artificial intelligence, big data, IoT, social media analytics, cloud computing, and mobile technologies, cause transformations in the environmental sustainability domains (Tables 1-4). We observed that digital technologies enable transformations in different areas of environmental sustainability, such as pollution control [51][52][53][54][55], waste management [27,[56][57][58][59][60][61], sustainable production [62][63][64][65][66][67][68], and urban sustainability [69][70][71][72][73][74]. All these studies show how digital technologies are transforming the different aspects of environmental sustainability. ...
... There were also studies on how IoT technology is being used to measure and control air pollution [94]. IoT sensors provide real-time monitoring information and demonstrate great potential as an effective tool to understand the PM2.5 plume movement with temporal variation and geo-specific location, which can lead to better air quality [54]. Social media has been used to measure the impacts of air pollution and disaster management. ...
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Digital transformation refers to the unprecedented disruptions in society, industry, and organizations stimulated by advances in digital technologies such as artificial intelligence, big data analytics, cloud computing, and the Internet of Things (IoT). Presently, there is a lack of studies to map digital transformation in the environmental sustainability domain. This paper identifies the disruptions driven by digital transformation in the environmental sustainability domain through a systematic literature review. The results present a framework that outlines the transformations in four key areas: pollution control, waste management, sustainable production, and urban sustainability. The transformations in each key area are divided into further sub-categories. This study proposes an agenda for future research in terms of organizational capabilities, performance, and digital transformation strategy regarding environmental sustainability.
... Some studies have examined how the Internet of Things (IoT) can be used to measure and control air pollution. For example, Idrees and Zheng (2020) showed that an IoT sensor with real-time monitoring information and support was an effective tool to identify fine particulate matter with diameters generally 2.5 µm and smaller (PM2.5) and could be used to predict changes in dynamic trends (Kanabkaew et al. 2019). Zuo et al. (2018) found that a novel IoT and cloud-based approach could perform energy consumption evaluations and analyses of products. ...
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... The PM 0.1 real-time sensor in the internet of things (IoT) monitoring network (big data) will play an essential role in understanding the PM 0.1 plume migration and transportation, with temporal variations by geo-specific location [60]. The real-time and IoT sensor for PMs monitoring has been a vital tool, potentially becoming an integral part of air quality monitoring and management, especially during the haze episode of intensive biomass burning smoke in the SEA environment [61]. ...
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PM0.1 (particles with a diameter ≤ 0.1 µm), nanoparticles (NPs), or ultrafine particles (UFPs) were interchangeably used in the scientific communities. PM0.1 originated from both natural and human sources; however, PM0.1 and its effects on the environment, visibility, and human health to understanding air pollution levels, sources, and impacts in Southeast Asia (SEA) countries continue to be challenging. The concentrations of PM0.1 in most SEA countries are much worse than in western countries’ environments. A further motivation of this reviewed article is to provide a critical synthesis of the current knowledge and study of ambient PM0.1 in SEA cities. The primary influence of characteristics of PM0.1 appears to be local sources, including biomass burning and motor vehicles. Continuous monitoring of PM0.1 in mass and number concentration should be further understood. A critical review is of great importance to facilitating air pollution control policies and predicting the behavior of PM0.1 in SEA.
... The factors affecting the occurrence of rock burst are different, and the occurrence mechanism, monitoring, and early warning mechanism of rock burst are different [8]. Kanabkaew et al. conducted in-depth research on the characteristic changes of microseismic signals before and after rock burst disaster in hard rock and found that before rock burst, the microseismic signal strength will continue to increase; that is, microseismic signals with strong intensity will appear, and then there will be a trend of sudden energy drop, followed by rock burst or other ground pressure disasters [9]. ...
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In order to ensure the safe mining of kilometer mining working surface threatened by impact ground pressure, a metal mine ground pressure monitoring and early warning based on deep learning data analysis are proposed. This paper expounds the theoretical basis of rock burst, analyzes the inducing factors of deep well rock burst, analyzes and introduces the classification of rock burst, focuses on the progressive failure process of rock burst and standard of rock fracture depth of deep ore and rock in a metal mine, carries out triaxial stress-strain test on the core in the laboratory, and evaluates the tendency of rock burst for deep ore and rock through elastic strain generation, strength brittleness coefficient method, and deformation brittleness coefficient method. The real-time monitoring and early warning system of rock burst can monitor the dynamic change of advance stress in the working face in real time and give real-time early warning to the dangerous area and degree of rock burst. The experimental results show that the working face enters the fault affected area when it advances 170 m in front of the fault. When the working face advances to 100 m in front of the fault, it enters the high stress area formed by the superposition of fault tectonic stress and mining stress. When the working face advances to 40 m in front of the fault, the stress reaches the maximum. Therefore, the system can accurately predict the impact risk area and its risk degree and realize the safe mining of high impact risk face.
... Interactions between air pollutants and meteorological parameters are complex [35]; however, the meteorological data can help us to understand the formation and destruction of the pollutants both by temporal and spatial distribution [36]. Correlation matrices were used for both linear and polynomial regressions for all air pollutants and meteorological data to explore their relationships [37]. ...
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... In both cases we found a high linearity between the PMS 7003 reading and the gravimetric and its 1-h equivalent method (R 2 > 0.90); we also found that the PMS 7003 data needs to be multiply by 1.4 to be gravimetric equivalent. This value (1.4) is very close to the value 1.7 found by Kanabkaew et al. (2019) also for PMS 7003. Our calibration was done in Tenerife, Canary Island, where PM x composition is expected to be close to that of Cape Verde, dominated by sea salt and dust, with rather low amounts of combustion aerosols. ...
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... Such a network is suitable for assessing the exposure to urban air pollution. Kanabkaew et al. [8] analyzed the air quality of Mae Sot City, Thailand by using monitoring data from a low-cost IoT PM 2.5 sensor system. Gao et al. [4] used a distributed network of low-cost sensors to measure the spatiotemporal variations of fine particulate matter (PM 2.5 ) in Xi'an, China. ...
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... The underlying idea is that a cost-effective approach for air quality monitoring would be the implementation of mixed networks involving both reference-grade monitors as well as emerging sensor technologies (Cao et al., 2020;Mead et al., 2013). Sensor use may affect a wide range of possible applications including high resolution spatial mapping and hot-spot identification (Gulia et al., 2020), emergency intervention, near-source monitoring (Kanabkaew et al., 2019), mobile and personal monitoring (Duvall et al., 2016;Jovašević-Stojanović et al., 2015;Park et al., 2020). Epidemiological studies would also greatly benefit from more detailed information in terms of exposure assessment (Larkin and Hystad, 2017). ...
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Open burning emissions strongly influence smoke haze problems in Southeast Asia (SEA). The main objective of this study is to investigate the percent contribution of emissions from local and transboundary on air pollutant concentrations, particularly PM10 (particulate matter with a diameter of less than 10 μm), using the potential source contribution function (PSCF). A three-day backward trajectory (BWT) analysis of air mass movements at the Chiang Mai Air Quality Monitoring (CM-AQM) station in the dry season (February–April) during the years 2010–2015 was run and clustered. It was found that the air masses mainly originated from the southwest of the CM-AQM station. The correlation between the PM10 concentration and the number of active fires during the three-day BWT showed the highest correlation in April (R² = 0.64). The PSCF values showed that most of the high-potential sources (0.9–1.0) and emissions were transboundary from Myanmar (73.2%) and within Thailand (26.8%). The major open burning source during March and April was found in the agricultural areas of Myanmar, and the second-greatest source was found in the forested areas of Myanmar. However, the agricultural areas of Thailand contributed to the PM10 concentration in northern Thailand (NT) in February. Thus, this result shows that potential point sources of pollutants such as biomass burning, including those transported across national boundaries, can be investigated and determined their locations in the haze episodes of NT.
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Satellite-derived aerosol optical depth (AOD) has been proven effective for estimating ground-level particles with an aerodynamic diameter <2.5μm (PM2.5) concentrations. Using a time fixed effects regression model, we compared the capacity of two AOD sources, Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS), to estimate ground-level PM2.5 concentrations over a heavily polluted region in China. Regarding high-quality AOD data, the results show that the VIIRS model performs better than the MODIS model with respect to all model accuracy evaluation indexes (e.g., the coefficient of determination, R(2), of the VIIRS and MODIS models are 0.76 and 0.71 during model fitting and 0.72 and 0.66 in cross validation, respectively), the potential for capturing high PM2.5 concentrations, and the precision of annual and seasonal PM2.5 estimates. However, the spatiotemporal coverage of the high-quality VIIRS AOD is inferior to that of the MODIS AOD. We attempted to include medium-quality VIIRS AOD data to eliminate this, while exploring its influence on the performance of the VIIRS model. The results show that it improves the spatiotemporal coverage of the VIIRS AOD dramatically especially in winter, although a decline in model accuracy occurred. Compared to the MODIS model, the VIIRS model with both high-quality and medium-quality AOD data performs comparably or even better with respect to some model accuracy evaluation indexes (e.g., the model overfitting degree of the VIIRS and MODIS models are 7.46% and 5.82%, respectively), the potential for capturing high PM2.5 concentrations, and the precision of annual and seasonal PM2.5 estimates. Nevertheless, the VIIRS models did not perform as well as the MODIS model in summer. This study reveals the advantages and disadvantages of the MODIS and VIIRS AOD in simulating ground-level PM2.5 concentrations, promoting research on satellite-based PM2.5 estimates.
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Low-cost air quality sensors offer high-resolution spatiotemporal measurements that can be used for air resources management and exposure estimation. Yet, such sensors require frequent calibration to provide reliable data, since even after a laboratory calibration they might not report correct values when they are deployed in the field, due to interference with other pollutants, as a result of sensitivity to environmental conditions and due to sensor aging and drift. Field calibration has been suggested as a means for overcoming these limitations, with the common strategy involving periodical collocations of the sensors at an air quality monitoring station. However, the cost and complexity involved in relocating numerous sensor nodes back and forth, and the loss of data during the repeated calibration periods make this strategy inefficient. This work examines an alternative approach, a node-to-node (N2N) calibration, where only one sensor in each chain is directly calibrated against the reference measurements and the rest of the sensors are calibrated sequentially one against the other while they are deployed and collocated in pairs. The calibration can be performed multiple times as a routine procedure. This procedure minimizes the total number of sensor relocations, and enables calibration while simultaneously collecting data at the deployment sites. We studied N2N chain calibration and the propagation of the calibration error analytically, computationally and experimentally. The in-situ N2N calibration is shown to be generic and applicable for different pollutants, sensing technologies, sensor platforms, chain lengths, and sensor order within the chain. In particular, we show that chain calibration of three nodes, each calibrated for a week, propagate calibration errors that are similar to those found in direct field calibration. Hence, N2N calibration is shown to be suitable for calibration of distributed sensor networks.
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This paper presents a comprehensive assessment of historical (1990–2010) global anthropogenic particulate matter (PM) emissions including the consistent and harmonized calculation of mass-based size distribution (PM1, PM2. 5, PM10), as well as primary carbonaceous aerosols including black carbon (BC) and organic carbon (OC). The estimates were developed with the integrated assessment model GAINS, where source- and region-specific technology characteristics are explicitly included. This assessment includes a number of previously unaccounted or often misallocated emission sources, i.e. kerosene lamps, gas flaring, diesel generators, refuse burning; some of them were reported in the past for selected regions or in the context of a particular pollutant or sector but not included as part of a total estimate. Spatially, emissions were calculated for 172 source regions (as well as international shipping), presented for 25 global regions, and allocated to 0.5° × 0.5° longitude–latitude grids. No independent estimates of emissions from forest fires and savannah burning are provided and neither windblown dust nor unpaved roads emissions are included. We estimate that global emissions of PM have not changed significantly between 1990 and 2010, showing a strong decoupling from the global increase in energy consumption and, consequently, CO2 emissions, but there are significantly different regional trends, with a particularly strong increase in East Asia and Africa and a strong decline in Europe, North America, and the Pacific region. This in turn resulted in important changes in the spatial pattern of PM burden, e.g. European, North American, and Pacific contributions to global emissions dropped from nearly 30 % in 1990 to well below 15 % in 2010, while Asia's contribution grew from just over 50 % to nearly two-thirds of the global total in 2010. For all PM species considered, Asian sources represented over 60 % of the global anthropogenic total, and residential combustion was the most important sector, contributing about 60 % for BC and OC, 45 % for PM2. 5, and less than 40 % for PM10, where large combustion sources and industrial processes are equally important. Global anthropogenic emissions of BC were estimated at about 6.6 and 7.2 Tg in 2000 and 2010, respectively, and represent about 15 % of PM2. 5 but for some sources reach nearly 50 %, i.e. for the transport sector. Our global BC numbers are higher than previously published owing primarily to the inclusion of new sources. This PM estimate fills the gap in emission data and emission source characterization required in air quality and climate modelling studies and health impact assessments at a regional and global level, as it includes both carbonaceous and non-carbonaceous constituents of primary particulate matter emissions. The developed emission dataset has been used in several regional and global atmospheric transport and climate model simulations within the ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants) project and beyond, serves better parameterization of the global integrated assessment models with respect to representation of black carbon and organic carbon emissions, and built a basis for recently published global particulate number estimates.
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Low-cost sensor technology can potentially revolutionise the area of air pollution monitoring by providing high-density spatiotemporal pollution data. Such data can be utilised for supplementing traditional pollution monitoring, improving exposure estimates, and raising community awareness about air pollution. However, data quality remains a major concern that hinders the widespread adoption of low-cost sensor technology. Unreliable data may mislead unsuspecting users and potentially lead to alarming consequences such as reporting acceptable air pollutant levels when they are above the limits deemed safe for human health. This article provides scientific guidance to the end-users for effectively deploying low-cost sensors for monitoring air pollution and people’s exposure, while ensuring reasonable data quality. We review the performance characteristics of several low-cost particle and gas monitoring sensors and provide recommendations to end-users for making proper sensor selection by summarizing the capabilities and limitations of such sensors. The challenges, best practices, and future outlook for effectively deploying low-cost sensors, and maintaining data quality is also discussed. For data quality assurance, a two-stage sensor calibration process is recommended, which includes laboratory calibration under controlled conditions by the manufacturer supplemented with routine calibration checks performed by the end-user under final deployment conditions. For large sensor networks where routine calibration checks are impractical, statistical techniques for data quality assurance should be utilised. Further advancements and adoption of sophisticated mathematical and statistical techniques for sensor calibration, fault detection, and data quality assurance can indeed help to realise the promised benefits of a low-cost air pollution sensor network.
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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
Low-cost, light-scattering-based particulate matter (PM) sensors are becoming more widely available and are being increasingly deployed in ambient and indoor environments because of their low cost and ability to provide high spatial and temporal resolution PM information. Researchers have begun to evaluate some of these sensors under laboratory and environmental conditions. In this study, a low-cost, particulate matter sensor (Plantower PMS 1003/3003) used by a community air-quality network is evaluated in a controlled wind-tunnel environment and in the ambient environment during several winter-time, cold-pool events that are associated with high ambient levels of PM. In the wind-tunnel, the PMS sensor performance is compared to two research-grade, light-scattering instruments, and in the ambient tests, the sensor performance is compared to two federal equivalent (one tapered element oscillating microbalance and one beta attenuation monitor) and gravimetric federal reference methods (FEMs/FRMs) as well as one research-grade instrument (GRIMM). The PMS sensor response correlates well with research-grade instruments in the wind-tunnel tests, and its response is linear over the concentration range tested (200-850 μg/m(3)). In the ambient tests, this PM sensor correlates better with gravimetric methods than previous studies with correlation coefficients of 0.88. However additional measurements under a variety of ambient conditions are needed. Although the PMS sensor correlated as well as the research-grade instrument to the FRM/FEMs in ambient conditions, its response varies with particle properties to a much greater degree than the research-grade instrument. In addition, the PMS sensors overestimate ambient PM concentrations and begin to exhibit a non-linear response when PM2.5 concentrations exceed 40 μg/m(3). These results have important implications for communicating results from low-cost sensor networks, and they highlight the importance of using an appropriate correction factor for the target environmental conditions if the user wants to compare the results to FEM/FRMs.
Article
Air quality in cities of Sub-Saharan African (SSA) countries has deteriorated with the situation driven by rapid population growth and its attendant increased vehicle ownership, increased use of solid fuels for cooking and heating, and poor waste management practices. Industrial expansion in these cities is also a major contributor to the worsening air pollution. Exposure to ambient air pollution is a major threat to human health in SSA with 176,000 deaths and 626,000 DALYs in the region attributable to ambient air pollution exposure. These estimates are however likely to be much higher than reported due to the limited data emanating from the region. Recently, the adoption of the World Health Assembly resolution on air pollution and health, and Sustainable Development Goals are a welcome boost for urban air pollution control efforts in SSA. In this article, we have outlined within the broad framework of these international policy instruments, measures for addressing urban air pollution and its associated health impacts in SSA sustainably.
Article
In this research, we examine the effects of PM10 and CO2 air pollutants on infant mortality and life expectancy at birth, in 60 developing countries during the period 1990–2010 by using unbalanced panel data and recursive simultaneous equations model. Our results show that the gains are obtained in the health status through the improvement in socio-economic conditions can be canceled by PM10 and CO2 air pollutants. Therefore, health policies which just focus on socio-economic aspects and ignore the adverse impacts of the air pollution may do little in efforts directed to improve the current health status of developing countries.
Article
Inhalation of air pollution during transport is an important exposure pathway, especially for certain modes of travel and types of particles. We measured concentrations of particulate air pollution (particle number [PN], black carbon [BC], fine particles [PM2.5], particle size) using a mobile, bicycle-based monitoring platform during morning and afternoon rush-hour to explore patterns of exposure while cycling (34 days between August 14 and October 16, 2012 in Minneapolis, MN). Measurements were geo-located at 1 s intervals along 3 prescribed monitoring routes totaling 85 h (1426 km) of monitoring. Mean morning [afternoon] on-road concentrations were 32,500 [16,600] pt cm-3, 2.5 [0.7] μg m-3 BC, 8.7 [8.3] μg m-3 PM2.5, and 42 [39] nm particle diameter. Concentrations were correlated with street functional class and declined within small distances from a major road (e.g., for PN and BC, mean concentration decreased ~20% by moving 1 block away from major roads to adjacent local roads). We estimate the share of on-bicycle exposure attributable to near-traffic emissions (vs. regional pollution) is ~50% for PN and BC; ~25% for PM2.5. Regression models of instantaneous traffic volumes, derived from on-bicycle video recordings of nearby traffic, quantify the increase in particle-concentrations associated with each passing vehicle; for example, trucks were associated with acute, high concentration exposure events (average concentration-increase per truck: 31,000 pt cm-3, 1.0 μg m-3 PM2.5, 1.6 μg m-3 BC). Our findings could be used to inform design of low-exposure bicycle networks in urban areas.
Article
Long-term analysis of tropospheric nitrogen dioxide (NO2) columns retrieved from GOME, SCIAMACHY, OMI and GOME-2 satellites, carbon monoxide (CO) columns from MOPITT satellite, and aerosol optical depths (AODs) from MODIS satellite was performed for Southeast Asian countries including Japan and China during 1996–2012. The results show that significant increasing levels of tropospheric NO2 columns can be clearly observed during the study period, especially above the eastern regions of China. The cities located in different latitude zones present the seasonal cycle of NO2 columns, CO columns, and AODs differently. For the cities located around mid-latitude zone, the maximum levels of NO2 and CO columns can be observed in the winter (November–March) and the minimum in the summer (June–September). On the contrary, the maximum levels for the cities near Equator zone are revealed in dry season (June–October). In the case of AODs, the maximum peaks normally occur during biomass burning season. Ground monitoring concentrations of NO2, CO, and PM10 were also comparably analyzed with satellite NO2 columns, CO columns, and AODs, respectively. Anthropogenic and biomass burning emissions were derived to investigate the consistency with satellite retrievals. The results show that satellite observations are able to capture the trend and seasonal variability of the emissions and ground concentrations. The model simulations were conducted using CMAQ model. Generally, simulated model results agree well with those retrieved from satellite measurements for spatial distribution and seasonal pattern. However, the modeled results underestimate satellite data probably due to the inaccuracy in emission inventories, the inaccuracy of spatial and temporal allocations, and the uncertainties in the satellite retrievals.
Article
Fine particulate matter is one of the key global pollutants affecting human health. Satellite and ground- based monitoring technologies as well as chemical transport models have advanced significantly in the past 50 years, enabling improved understanding of the sources of fine particles, their chemical composition, and their effect on human and environmental health. The ability of air pollution to travel across country and geographic boundaries makes particulate matter a global problem. However, the variability in monitoring technologies and programs and poor data availability make global comparison difficult. This paper summarizes fine particle monitoring, models that integrate ground-based and satellite-based data, and communications, then recommends steps for policymakers and scientists to take to expand and improve local and global indicators of particulate matter air pollution. One of the key set of recommendations to improving global indicators is to improve data collection by basing particulate matter monitoring design and stakeholder communications on the individual country, its priorities, and its level of development, while at the same time creating global data standards for inter-country com- parisons. When there are good national networks that produce consistent quality data that is shared openly, they serve as the foundation for better global understanding through data analysis, modeling, health impact studies, and communication. Additionally, new technologies and systems should be developed to expand personal air quality monitoring and participation of non-specialists in crowd- sourced data collections. Finally, support to the development and improvement of global multi- pollutant indicators of the health and economic effects of air pollution is essential to addressing improvement of air quality around the world.
Article
The air monitoring paradigm is rapidly changing due to advances in the development of portable, lower-cost air pollution sensors report high-time resolution data in near-real time along with supporting data and communication infrastructure. These changes are bringing forward opportunities to the traditional monitoring framework (supplementing ambient air monitoring and enhancing compliance monitoring) and also is expanding monitoring beyond this framework (personal exposure monitoring and community-based monitoring). Opportunities in each of these areas as well as corresponding challenges and potential solutions associated with development and implementation of air pollution sensors are discussed.
Article
Using 1 year of aerosol optical thickness (AOT) retrievals from the MODerate resolution Imaging Spectro-radiometer (MODIS) on board NASA's Terra and Aqua satellite along with ground measurements of PM2.5 mass concentration, we assess particulate matter air quality over different locations across the global urban areas spread over 26 locations in Sydney, Delhi, Hong Kong, New York City and Switzerland. An empirical relationship between AOT and PM2.5 mass is obtained and results show that there is an excellent correlation between the bin-averaged daily mean satellite and ground-based values with a linear correlation coefficient of 0.96. Using meteorological and other ancillary datasets, we assess the effects of wind speed, cloud cover, and mixing height (MH) on particulate matter (PM) air quality and conclude that these data are necessary to further apply satellite data for air quality research. Our study clearly demonstrates that satellite-derived AOT is a good surrogate for monitoring PM air quality over the earth. However, our analysis shows that the PM2.5–AOT relationship strongly depends on aerosol concentrations, ambient relative humidity (RH), fractional cloud cover and height of the mixing layer. Highest correlation between MODIS AOT and PM2.5 mass is found under clear sky conditions with less than 40–50% RH and when atmospheric MH ranges from 100 to 200 m. Future remote sensing sensors such as Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) that have the capability to provide vertical distribution of aerosols will further enhance our ability to monitor and forecast air pollution. This study is among the first to examine the relationship between satellite and ground measurements over several global locations.
Article
Advances in satellite sensors have provided new datasets for monitoring air quality at urban and regional scales. Qualitative true color images and quantitative aerosol optical depth data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Terra satellite were compared with ground-based particulate matter data from US Environmental Protection Agency (EPA) monitoring networks covering the period from 1 April to 30 September, 2002. Using both imagery and statistical analysis, satellite data enabled the determination of the regional sources of air pollution events, the general type of pollutant (smoke, haze, dust), the intensity of the events, and their motion. Very high and very low aerosol optical depths were found to be eliminated by the algorithm used to calculate the MODIS aerosol optical depth data. Correlations of MODIS aerosol optical depth with ground-based particulate matter were better in the eastern and Midwest portion of the United States (east of 100°W). Data were patchy and had poorer correlations in the western US, although the correlation was dependent on location. This variability is likely due to a combination of the differences between ground-based and column average datasets, regression artifacts, variability of terrain, and MODIS cloud mask and aerosol optical depth algorithms. Preliminary analysis of the algorithms indicated that aerosol optical depth measurements calculated from the sulfate-rich aerosol model may be more useful in predicting ground-based particulate matter levels, but further analysis would be required to verify the effect of the model on correlations. Overall, the use of satellite sensor data such as from MODIS has significant potential to enhance air quality monitoring over synoptic and regional scales.