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

Atmospheric pollution is a serious problem in many countries, including India, and it is generally considered as an urban issue. To fill the knowledge gap about particulate pollution and its adverse health effects in rural India for well-informed region-specific policy interventions, we present new insights on the rural pollution of India in terms of PM2.5. Here, we analyse PM2.5 pollution and its associated health burden in rural India using satellite and reanalyses data for the period 2000-2019. We observe a gradual and consistent rise of atmospheric pollution in rural areas of India. The highest PM2.5 levels are observed in Indo-Gangetic Plain (IGP) during winter and post-monsoon seasons (107.0 ± 17.0 and 91.0 ± 21.7 μg/m3, respectively). A dipole reversal in seasonal trends between winter and post-monsoon seasons is found for black carbon (BC) and organic carbon (OC) in the rural IGP. The rural North West India (NWI) experiences elevated PM2.5 concentrations due to dust storms, while the rural hilly region (HR) in the Himalaya remains the least polluted region in India. The highest PM2.5 associated cardiopulmonary mortality in 2019 is observed in the rural IGP districts (1000-5100), whereas the highest mortality due to lung cancer at district level accounts for 10-60 deaths. The highest mortality attributed to PM2.5 is observed in districts of Uttar Pradesh, Bihar, West Bengal, Punjab, Haryana and Rajasthan. The priority-wise segregation of states as per World Health Organisation (WHO) Interim targets (ITs), as assessed in this study, might be helpful in implementation and development of policies in phases. We, therefore, present the first detailed study on the PM2.5 pollution in rural India, and provide valuable insights on its distribution, variability, sources and associated mortality, and emphasize the need for addressing this issue to protect public health.

Integrating satellite and model data to explore spatial-temporal changes in aerosol optical properties and their meteorological relationships in northwest India

This study aims to analyze the temporal and spatial distribution of Aerosol Optical Properties across Northwest India using aerosol data from MODIS (Moderate Resolution Imaging Spectroradiometer) and OMI (Ozone Monitoring Instrument) sensors from 2003 to 2022. Therefore, this study investigated the decadal, interannual, and seasonal changes in aerosol optical properties, vegetation index, and meteorological parameters in the northwest Indian region (8 boxes). Using GIOVANNI (Goddard Earth Sciences Data and Information Services Center (GES DISC) Online Visualization and Analysis Infrastructure), we retrieved daily and monthly Aqua and Terra MODIS products of aerosol optical depth (AOD), Angstrom exponent (AE), normalized difference vegetation index (NDVI), and OMI aerosol index (AI) to examine the spatiotemporal variations by using statistical approaches. The results demonstrated that the decadal averages of aerosol properties showed values of AOD 0.35 (Aqua) and 0.34 (Terra) and AE 1.20 (Aqua) and 1.10 (Terra) with the highest levels during the post-monsoon. Notably, the mean interannual concentrations of AOD and NDVI consistently surpass 0.3, and AE and AI exceed 1 in most locations, underscoring the persistence of high aerosol loading. Also, the study revealed a negative decadal change in AOD of about -8.24 %, while AE, AI, and NDVI showed positive decadal changes of about 9.24 %, 15.09 %, and 12.67 %, respectively. In addition, aerosol optical properties and local meteorology strongly correlated (-0.8 to +0.8). Principal Component Analysis (PCA) identifies meteorological parameters as significant drivers, with the first three components explaining over 70 % of the variation in aerosol optical properties. The NOAA HYSPLIT trajectory model suggests that the long-distance dust transport from the Arabian Peninsula frequently penetrates Gujarat province and then to northwest India. The results contributed to air quality management strategies and provided valuable insights into regional climate and air quality with the influence of meteorology.

Aerosol influence on cloud macrophysical and microphysical properties over the Tibetan Plateau and its adjacent regions

This study uses aerosol optical depth (AOD) and cloud properties data to investigate the influence of aerosol on the cloud properties over the Tibetan Plateau and its adjacent regions. The study regions are divided as the western part of the Tibetan Plateau (WTP), the Indo-Gangetic Plain (IGP), and the Sichuan Basin (SCB). All three regions show significant cloud effects under low aerosol loading conditions. In WTP, under low aerosol loading conditions, the effective radius of liquid cloud particles (LREF) decreases with the increase of aerosol loading, while the effective radius of ice cloud particles (IREF) and cloud top height (CTH) increase during the cold season. Increased aerosol loading might inhibit the development of warm rain processes, transporting more cloud droplets above the freezing level and promoting ice cloud development. During the warm season, under low aerosol loading conditions, both the cloud microphysical (LREF and IREF) and macrophysical (cloud top height and cloud fraction) properties increase with the increase of aerosol loading, likely due to higher dust aerosol concentration in this region. In IGP, both LREF and IREF increase with the increase in aerosol loading during the cold season. In SCB, LREF increases with the increase in aerosol loading, while IREF decreases, possibly due to the higher hygroscopic aerosol concentration in the SCB during the cold season. Meteorological conditions also modulate the aerosol-cloud interaction. Under different convective available potential energy (CAPE) and relative humidity (RH) conditions, the influence of aerosol on clouds varies in the three regions. Under low CAPE and RH conditions, the relationship between LREF and aerosol in both the cold and warm seasons is opposite in the WTP: LREF decreases with the increase of aerosol in the cold season, while it increases in the warm season. This discrepancy may be attributed to a difference in the moisture condition between the cold and warm seasons in this region. In general, the influence of aerosols on cloud properties in TP and its adjacent regions is characterized by significant nonlinearity and spatial variability, which is likely related to the differences in aerosol types and meteorological conditions between different regions.

Application of machine learning (individual vs stacking) models on MERRA-2 data to predict surface PM2.5 concentrations over India

The spatial coverage of PM2.5 monitoring is non-uniform across India due to the limited number of ground monitoring stations. Alternatively, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), is an atmospheric reanalysis data used for estimating PM2.5. MERRA-2 does not explicitly measure PM2.5 but rather follows an empirical model. MERRA-2 data were spatiotemporally collocated with ground observation for validation across India. Significant underestimation in MERRA-2 prediction of PM2.5 was observed over many monitoring stations ranging from -20 to 60 μg m-3. The utility of Machine Learning (ML) models to overcome this challenge was assessed. MERRA-2 aerosol and meteorological parameters were the input features used to train and test the individual ML models and compare them with the stacking technique. Initially, with 10% of randomly selected data, individual model performance was assessed to identify the best model. XGBoost (XGB) was the best model (r2 = 0.73) compared to Random Forest (RF) and LightGBM (LGBM). Stacking was then applied by keeping XGB as a meta-regressor. Stacked model results (r2 = 0.77) outperformed the best standalone estimate of XGB. Stacking technique was used to predict hourly and daily PM2.5 in different regions across India and each monitoring station. The eastern region exhibited the best hourly prediction (r2 = 0.80) and substantial reduction in Mean Bias (MB = -0.03 μg m-3), followed by the northern region (r2 = 0.63 and MB = -0.10 μg m-3), which showed better output due to the frequent observation of PM2.5 >100 μg m-3. Due to sparse data availability to train the ML models, the lowest performance was for the central region (r2 = 0.46 and MB = -0.60 μg m-3). Overall, India's PM2.5 prediction was good on an hourly basis compared to a daily basis using the ML stacking technique.

India's photovoltaic potential amidst air pollution and land constraints

India aims for ambitious solar energy goal to fulfill its climate commitment but there are limited studies on solar resource assessment considering both environmental and land availability constraints. The present work attempts to address this issue using satellite-derived air pollution, radiation, and land use data over the Indian region. Surface insolation over India has been decreasing at a rate of -0.29 ± 0.19 Wm-2 y-1 between 2001 and 2018. Solar resources over nearly 98%, 40%, and 39% of the Indian landmass are significantly impacted by aerosols, clouds, and both aerosols and clouds respectively. Only 29.3% of the Indian landmass is presently suitable for effective solar photovoltaic harnessing, but this is further declining by -0.21% annually, causing a presumptive loss of 50 GW solar potential, translating 75 TWh power generation. Lowering two decades of aerosol burden can make 8% additional landmass apt for photovoltaic use. Alleviating aerosol-induced dimming can fast-track India's solar energy expansion.

Source Contributions to Fine Particulate Matter and Attributable Mortality in India and the Surrounding Region

Fine particulate matter (PM_2.5) exposure is a leading mortality risk factor in India and the surrounding region of South Asia. This study evaluates the contribution of emission sectors and fuels to PM_2.5 mass for 29 states in India and 6 surrounding countries (Pakistan, Bangladesh, Nepal, Bhutan, Sri Lanka, and Myanmar) by combining source-specific emission estimates, stretched grid simulations from a chemical transport model, high resolution hybrid PM_2.5, and disease-specific mortality estimates. We find that 1.02 (95% Confidence Interval (CI): 0.78-1.26) million deaths in South Asia attributable to ambient PM_2.5 in 2019 were primarily from three leading sectors: residential combustion (28%), industry (15%), and power generation (12%). Solid biofuel is the leading combustible fuel contributing to the PM_2.5-attributable mortality (31%), followed by coal (17%), and oil and gas (14%). State-level analyses reveal higher residential combustion contributions (35%-39%) in states (Delhi, Uttar-Pradesh, Haryana) with high ambient PM_2.5 (>95 μg/m³). The combined mortality burden associated with residential combustion (ambient) and household air pollution (HAP) in India is 0.72 million (95% CI:0.54-0.89) (68% attributable to HAP, 32% attributable to residential combustion). Our results illustrate the potential to reduce PM_2.5 mass and improve population health by reducing emissions from traditional energy sources across multiple sectors in South Asia.

Driving factors behind the continuous increase of long-term PM2.5-attributable health burden in India using the high-resolution global datasets from 2001 to 2020

Air pollution is the fourth leading global risk factor, whereas in India air pollution is reported as the highest risk factor with millions of premature deaths every year. Despite implementation of several air pollution control plans, PM_2.5 levels over India have not noticeably reduced. PM_2.5-associated health burdens in India have increased significantly in past decades. A fine resolution (0·01° × 0·01°) analysis of PM_2.5-attribulable premature deaths (rather than the coarse-level analysis) may elucidate the reason for this increase and inform and effective start-of-the-art state-level and national emission control strategies. This study quantified the spatiotemporal dynamics of PM_2.5-attributable premature deaths from 2001 to 2020 and applied a decomposition analysis to dissect the contribution of various associated parameters, such as PM_2.5 concentration, population distribution and disease-specific baseline death rate. Results show significant spatiotemporal variations of PM_2.5 and associated health burden in India. During the study period, population weighted PM_2.5 value increased from 46.0 to 59.5 μg/m³ and associated non-communicable death increased around 87.6 %, from 1050 [95 % (CI): 880-1210] thousand to 1970 (95 % CI: 1658-2259) thousand. The states of Uttar Pradesh, Bihar, West Bengal, Maharashtra, Rajasthan, and Madhya Pradesh had the highest PM_2.5-attributable deaths. In these states, non-accidental deaths increased from 232.1, 112.7, 81.4, 79.1, 66.3 and 58.5 thousand in 2001 to 424.1, 226.7, 156.2, 154.5, 123.3 and 119.7 thousand in 2020. In per capita population (/10⁵ population), the highest PM_2.5-attributable deaths were observed in Delhi, Uttar Pradesh, Bihar, Haryana and Punjab. Throughout the study period, demographic changes outweighed the health burden and were responsible for ~62.8 % increase of PM_2.5-related non-accidental deaths across India, whereas the change in PM_2.5 concentration influenced only 18.7 %. The change in baseline mortality rate impacts differently for the estimation of disease-specific mortality changes. Our findings suggest more dynamic and comprehensive policies at state-specific level, especially for North India is very indispensable for the overall decrease of PM_2.5-related deaths in India.

Temporal and Spatial Variations of Satellite-Based Aerosol Optical Depths, Angstrom Exponent, Single Scattering Albedo, and Ultraviolet-Aerosol Index over Five Polluted and Less-Polluted Cities of Northern India: Impact of Urbanization and Climate Change

It is widely acknowledged that factors such as population growth, urbanization's quick speed, economic growth, and industrialization all have a role in the atmosphere's rising aerosol concentration. In the current work, we assessed and discussed the findings of a thorough analysis of the temporal and spatial variations of satellite-based aerosol optical parameters such as Aerosol Optical Depth (AOD), Angstrom Exponent (AE), Single Scattering Albedo (SSA), and Ultraviolet-Aerosol Index (UV-AI), and their concentration have been investigated in this study over five polluted and less-polluted cities of northern India during the last decade 2011–2020. The temporal variation of aerosol optical parameters for AOD ranging from 0.2 to 1.8 with decadal mean 0.86 ± 0.36 for Patna region shows high value with a decadal increasing trend over the study area due to rise in aerosols combustion of fossil fuels, huge vehicles traffic, and biomass over the past ten years. The temporal variation of AE ranging from 0.3 to 1.8 with decadal mean 1.72 ± 0.11 for Agra region shows high value as compared to other study areas, which indicates a comparatively higher level of fine-mode aerosols at Agra. The temporal variation of SSA ranging from 0.8 to 0.9 with decadal mean 0.92 ± 0.02 for SSA shows no discernible decadal pattern at any of the locations. The temporal variation of UV-AI ranging from -1.01 to 2.36 with decadal mean 0.59 ± 0.06 for UV-AI demonstrates a rising tendency, with a noticeable rise in Ludhiana, which suggests relative dominance of absorbing dust aerosols over Ludhiana. Further, to understand the impact of emerging activities, analyses were done in seasonality. For this aerosol climatology was derived for different seasons, i.e., Winter, Pre-Monsoon, Monsoon, and Post-Monsoon. High aerosol was observed in Winter for the study areas Patna, Delhi, and Agra which indicated the particles major dominance of burning aerosol from biomass; and the worst in Monsoon and Post-Monsoon for the Tehri Garhwal and Ludhiana study areas which indicated most of the aerosol concentration is removed by rainfall. After that, we analyzed the correlation among all the parameters to better understand the temporal and spatial distribution characteristics of aerosols over the selected region. The value of r for AOD (550 nm) for regions 2 and 1(0.80) shows a strong positive correlation and moderately positive for the regions 3 and 1 (0.64), mostly as a result of mineral dust carried from arid western regions. The value of r for AE (412/470 nm) for region 3 and (0.40) shows a moderately positive correlation, which is the resultant of the dominance of fine-mode aerosol and negative for the regions 5 and 1 (− 0.06). The value of r for SSA (500 nm) for regions 2 and 1 (0.63) shows a moderately positive correlation, which explains the rise in big aerosol particles, which scatters sun energy more efficiently, and the value of r for UV-AI for regions 1 and 2 shows a strong positive correlation (0.77) and moderately positive for the regions 3 and 1 (0.46) which indicates the absorbing aerosols present over the study region.

Evaluation of the simulated aerosol optical properties over India: COALESCE model inter-comparison of three GCMs with ground and satellite observations

Within the framework of COALESCE project (Carbonaceous aerosol emissions, source apportionment, and climate impacts) initiative, spatio-temporal distribution of aerosol optical properties from three general circulation models are evaluated against aerosol data from satellite observations (MODIS and CALIPSO) and ground-based measurements (AERONET) for the period 2005-2014. The GCMs, NICAM-SPRINTARS (N-S), ECHAM6.3-HAM2.3 (E-H), CAM5.3 (CAM), input with identical emissions from the SMoG-India-v1 emission inventory over India nested in the CEDS global inventory, including all emission sectors except sea salt and soil dust. The annual mean total aerosol optical depth (AOD) averaged over the Indian land region is 0.38, 0.27, and 0.17 from the N-S, CAM, and E-H models respectively, while the annual mean value from the MODIS observational dataset is 0.43. Single scattering albedo predicted by E-H is lower compared to CAM and N-S while model predictions of Angstrom exponent are closer to MERRA2 dataset. However, the average total aerosol column burden over Indian landmass simulated by the models is very close and comparable to the reanalysis results. Statistical analysis of AOD between model and AERONET measurements at nine sites shows that the root mean square error varies from 0.1 to 0.4 and the index of agreement (average value) is ∼0.4. The aerosol emission and transport models, methodology for calculation of aerosol optical properties and their mixing states contributes to the diversity in the results from various models. The present study provides an analysis of limitations and uncertainties contributing to the differences between the simulations and observations, and the inter-model diversity.

Analysis and Variation of the Maiac Aerosol Optical Depth in Underexplored Urbanized Area of National Capital Region, India

Aerosol monitoring is the emerging application field of satellite remote sensing. As a satellite-based indicator of aerosol concentration, aerosol optical depth (AOD) can aid in assessing the crucial effects of aerosols on the global environment. Among various satellite-based aerosol product, Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (C6), Multiangle Implementation of Atmospheric Correction (MAIAC) aerosol product (1 km resolution) has still untapped potential in Indian regions. Considering the importance of regional validation of such high-resolution aerosol product, the present study attempts to fill this gap by validating MAIAC aerosol estimates (AODMAIAC) in highly polluted districts (Faridabad, Ghaziabad, Gautam Budh Nagar, Gurugram) of National Capital Region (NCR) with heavy aerosol loading using limited AErosol RObotic NETwork (AERONET) observations obtained from AERONET sites at Amity University (AU) and Gual Pahari (GP). Such evaluation of satellite-retrieved aerosol product with ground data confirms its practicality based on retrieval errors (Expected Error (EE) values (EE = 0.05 + 15 %*AOD) (EE: 78.85 % at AU, 73.58 % at GP), root mean square error (RMSE) values (RMSE: 0.15 at AU, 0.24 at GP), and correlation coefficient (R) values (R: 0.86 at AU, 0.73 at GP). The seasonal variation in AOD over the study area from 2010-2019 reveals increasing trend of AOD in the monsoon and post-monsoon season due to natural and anthropogenic factors. In addition to contributing to a holistic assessment of MAIAC aerosol estimates as a recent, high-resolution aerosol product, present results provide a basis for further research into NCR aerosols.

Three-dimensional nature of summertime aerosols over South Asia

Three-dimensional (temporal-spatial-vertical) climatology of South Asian summertime (MAMJ, 2010-2019) aerosols and aerosol sub-types was explored using multiple high-resolution satellite-based observations and reanalysis dataset. Vertical stratification of aerosol layer and aerosol sub-types was identified using observation from space-borne lidar. Aerosol optical depth (AOD) was particularly high across the Indo-Gangetic Plain (IGP; AOD ± SD: 0.56 ± 0.12) and over eastern coast of India (AOD: 0.6-0.8), with prevalence of heterogeneous aerosol sub-types having strong spatial gradient. Clearly, aerosols over north-western arid part were highly absorbing (Ultra-violet Aerosol Index, UVAI > 0.80) and coarse (Ångström exponent, AE < 0.8), with an indication of desert/-mineral dust aerosols. In contrast, fine and moderate to non-absorbing aerosols (UVAI: 0.20-0.50) dominate from central to lower IGP, including in Bangladesh, with signature of anthropogenic emissions. Prevailing aerosols over twelve South Asian cities were classified into six aerosol sub-types constraining their particle size and UV-absorbing potential. Overall, mineral dust, smoke and urban aerosols were the three major aerosol sub-types that prevail across South Asia during summer. In particular, 58-70 % of retrieval days over Karachi and Multan were dust dominated; 57-64 % days were dust or urban aerosols dominated over Lahore, Delhi, Kanpur and Varanasi, and 56-77 % days were smoke or urban aerosols dominated over Dhaka, Kathmandu, Chennai, Mumbai, Colombo and Nagpur. Prevailing aerosols were vertically stratified as 50-70 % of total AOD was retrieved <2 km from the surface except in few cities where 70-80 % of AOD was retrieved <3 km height. Mineral dust and/or urban aerosols emerged as the most abundant aerosol types near the surface (<1 km) in all the cities except in Chennai, with their abundance remained as a function of emission sources and geographical location.

Lightning Characteristics Over Humid Regions and Arid Regions and Their Association With Aerosols Over Northern India

The association between aerosol and lightning has been investigated with long-term decadal data (2005-2014) for lightning, aerosol optical depth (AOD), relative humidity, and effective cloud droplet size. To understand the complex relationship between aerosol and lightning, two different regions with different climatic and weather conditions, a humid region R1 (22°-29° N, 89°-92° E) and an arid region R2 (23°-28° N, 70°-76° E) of northern India, were chosen for the study domain. The results show that lightning activity was observed to occur more over the humid region R1, i.e., 1141 days (1/3 of total days), than over the arid region R2, i.e., 740 days (1/5 of total days). Also, over the humid region R1, the highest lightning flash density was recorded as nearly 4.6 × 10^-4 flashes/km²/day observed for 18 days (1.5%); on the contrary, over the arid region R2, the maximum lightning flash density was observed to be 2.5 × 10^-4 flashes/km²/day and occurred for about 22 days (2.9%). The analysis shows that a nonlinear relationship exists between aerosol and lightning with a highly associated influence of relative humidity. A very significant positive and negative co-relation that varies with relative humidity has been observed between AOD and lightning for both humid and arid regions. This shows relative humidity is the key factor in determining the increase or decrease of lightning activity. This study also shows that the larger the cloud droplet size, the higher the relative humidity and vice versa. This study emphasizes that aerosol concentration in the atmosphere influences cloud microphysics by modulating the size of cloud droplets and thereby regulating the lightning frequency. The atmospheric humidity is the driving factor in deciding the positive or negative co-relationship between aerosol and lightning.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00024-022-02981-6.

Changes in tropospheric ozone concentration over Indo-Gangetic Plains: the role of meteorological parameters

This study seeks to understand and quantify the changes in tropospheric ozone (O3) in lower troposphere (LT), middle troposphere (MT) and upper middle troposphere (UMT) over the Indo-Gangetic Plains (IGPs), India during the COVID-19 lockdown 2020 with that of pre-lockdown 2019. The gridded datasets of ozone from the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis product, ERA5 in combination with statistical interpolated (IDWs) surface NO2 observations, present a consistent picture and indicate a significant tropospheric ozone enhancement over IGP during COVID-19 lockdown restrictions in May 2020. The Paper also examines the influencing role of meteorological parameters on increasing ozone concentration. Over LT, an increase in O3 concentration (23%) is observed and in MT to UMT an enhancement of about 9–18% in O3 concentration have been seen during May 2020 with respect to May 2019. An investigation on causes of increasing  ozone concentration (35–85 ppbv) from MT to UMT during May 2020 reveals that there was significant rise (by 1–6%) in low cloud cover (LCC). Notably, higher LCC increases the backscattering of upward solar radiation from the top of the atmosphere. A positive difference of 5–25 W/m2 in upward solar radiation (USR) is observed across the entire study region. The result suggests that higher LCC significantly contributed to the enhanced USR. Thereby, resulting in higher photolysis rate that lead to an increase in mid tropospheric ozone concentration during May 2020. The results highlight the importance of LCC as an important pathway in ozone formation and aid in scientific understanding of it.

Impact of Aerosol and Cloud on the Solar Energy Potential over the Central Gangetic Himalayan Region

We examine the impact of atmospheric aerosols and clouds on the surface solar radiation and solar energy at Nainital, a high-altitude remote location in the central Gangetic Himalayan region (CGHR). For this purpose, we exploited the synergy of remote-sensed data in terms of ground-based AERONET Sun Photometer and satellite observations from the MODerate Resolution Imaging Spectroradiometer (MODIS) and the Meteosat Second Generation (MSG), with radiative transfer model (RTM) simulations and 1 day forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). Clouds and aerosols are one of the most common sources of solar irradiance attenuation and hence causing performance issues in the photovoltaic (PV) and concentrated solar power (CSP) plant installations. The outputs of RTM results presented with high accuracy under clear, cloudy sky and dust conditions for global horizontal (GHI) and beam horizontal irradiance (BHI). On an annual basis the total aerosol attenuation was found to be up to 105 kWh m−2 for the GHI and 266 kWh m−2 for BHI, respectively, while the cloud effect is much stronger with an attenuation of 245 and 271 kWh m−2 on GHI and BHI. The results of this study will support the Indian solar energy producers and electricity handling entities in order to quantify the energy and financial losses due to cloud and aerosol presence.

Effect of exposure to PM10 on child health: evidence based on a large-scale survey from 184 cities in India

Introduction

Air pollution is increasingly becoming a serious global public health concern. Prior studies examining the effect of air pollution on health have ignored the role of households’ hygienic practices and socioeconomic condition, which are key determinants of the health status of a country like India. This study examines the effects of air pollution, measured in levels of particulate matters of size below 10 µg/m³ (PM₁₀), on child-health outcomes after adjusting for hygiene practices.

Methods

Health data from the National Family Health Survey-4 (NFHS-4) and PM₁₀ levels provided by the Central Pollution Control Board were matched for 184 Indian towns/cities. Child health outcomes included neonatal mortality, post-neonatal mortality, premature births, children with symptoms of acute respiratory infections (ARI) and low birth weight. Multilevel mixed-effects models were used to estimate the risk associated with exposure to PM₁₀.

Result

Analyses based on 23 954 births found that every 10-unit increase in PM₁₀ level, increased the risk of neonatal mortality by 6% (adjusted RR (95% CI): 1.02 (1.02 to 1.09)), and the odds of symptoms of ARI among children by 7% (adjusted OR (95% CI): 1.07 (1.03 to 1.12)), and premature births by 8% (adjusted OR (95% CI): 1.08 (1.03 to 1.12)). There was no statistically significant difference in the effect of PM₁₀ on child health regardless of household’s hygienic practices. Effects of PM₁₀ on child health outcomes remained similar for cities whether or not they were part of the National Clean Air Program (NCAP).

Conclusion

Exposure to PM₁₀, regardless of hygienic practices, increases the risk of adverse child health outcomes. Study findings suggest that the focus of mitigating the effects of air pollution should be beyond the towns/cities identified under NCAP. Given the increasing industrialisation and urbanisation, a systemic, coherent approach is required to address the issue of air pollution in India.

Aerosols optical depth and Ångström exponent over different regions in Garhwal Himalaya, India

Aerosol optical depth (AOD) and Ångström exponent (AE) are observed to be important parameters in understanding the status of ambient aerosol concentration over a particular location and depend not only upon the local but also on the large-scale dynamics of the atmosphere. The present article analyses the AOD and AE parameters retrieved with Moderate Resolution Imaging Spectrometer (MODIS) and Multi-angle Imaging Spectro-Radiometer (MISR) instruments onboard satellites, for the upper (Chamoli) and foothill (Dehradun) regions of Garhwal Himalaya in Uttarakhand, India, from 2006 to 2015. Aerosol properties are investigated at monthly, seasonal, and annual scales. The monthly mean values of MODIS-derived AOD and AE were observed to be 0.18 (± 0.14) and 1.05 (± 0.43) respectively over the Dehradun region. The seasonal maximums in AOD with MODIS and MISR were observed as 0.23 ± 0.06 and 0.29 ± 0.07 respectively in the pre-monsoon season, and the minimum values (0.099 ± 0.02) were observed in the post-monsoon season, over the Dehradun region. In contrast, in the Chamoli region, the maximum AOD (MODIS) was 0.21 ± 0.06 observed in the monsoon season and the minimum was 0.036 ± 0.007 in the post-monsoon season. Over a decade, the AE for Chamoli and Dehradun was found to vary from 0.07 to 0.17 and from 0.14 to 0.20 respectively. The median AE for Chamoli and Dehradun was found to be 1.49 and 1.47 respectively, marking the dominance of fine mode particles of anthropogenic origin. Observations show the presence of dust and polluted dust resulting from the long-range transport from the west. The comparison of AOD values from the two sensors shows a significant correlation (0.73) with slightly higher values from MISR over the year. The results obtained are important in understanding the climatic implications due to the atmospheric aerosols over the abovementioned Himalayan region of Uttarakhand, India.

GOSAT CH4 Vertical Profiles over the Indian Subcontinent: Effect of a Priori and Averaging Kernels for Climate Applications

We examined methane (CH4) variability over different regions of India and the surrounding oceans derived from thermal infrared (TIR) band observations (TIR CH4) by the Thermal and Near-infrared Sensor for carbon Observation—Fourier Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observation SATellite (GOSAT) for the period 2009–2014. This study attempts to understand the sensitivity of the vertical profile retrievals at different layers of the troposphere and lower stratosphere, on the basis of the averaging kernel (AK) functions and a priori assumptions, as applied to the simulated concentrations by the MIROC4.0-based Atmospheric Chemistry-Transport Model (MIROC4-ACTM). We stress that this is of particular importance when the satellite-derived products are analyzed using different ACTMs other than those used as retrieved a priori. A comparison of modeled and retrieved CH4 vertical profiles shows that the GOSAT/TANSO-FTS TIR instrument has sufficient sensitivity to provide critical information about the transport of CH4 from the top of the boundary layer to the upper troposphere. The mean mismatch between TIR CH4 and model is within 50 ppb, except for the altitude range above 150 hPa, where the sensitivity of TIR CH4 observations becomes very low. Convolved model profiles with TIR CH4 AK reduces the mismatch to less than the retrieval uncertainty. Distinct seasonal variations of CH4 have been observed near the atmospheric boundary layer (800 hPa), free troposphere (500 hPa), and upper troposphere (300 hPa) over the northern and southern regions of India, corresponding to the southwest monsoon (July–September) and post-monsoon (October–December) seasons. Analysis of the transport and emission contributions to CH4 suggests that the CH4 seasonal cycle over the Indian subcontinent is governed by both the heterogeneous distributions of surface emissions and the influence of the global monsoon divergent wind circulations. The major contrast between monsoon, and pre- and post-monsoon profiles of CH4 over Indian regions are noticed near the boundary layer heights, which is mainly caused by seasonal change in local emission strength with a peak during summer due to increased emissions from the paddy fields and wetlands. A strong difference between seasons in the middle and upper troposphere is caused by convective transport of the emission signals from the surface and redistribution in the monsoon anticyclone of upper troposphere. TIR CH4 observations provide additional information on CH4 in the region compared to what is known from in situ data and total-column (XCH4) measurements. Based on two emission sensitivity simulations compared to TIR CH4 observations, we suggest that the emissions of CH4 from the India region were 51.2 ± 4.6 Tg year−1 during the period 2009–2014. Our results suggest that improvements in the a priori profile shape in the upper troposphere and lower stratosphere (UT/LS) region would help better interpretation of CH4 cycling in the earth’s environment.

Rapid increase in mortality attributable to PM2.5 exposure in India over 1998-2015

PM_2.5-attributable deaths and years of life lost (YLL) due to specific causes during 1998-2015 in India were estimated using the integrated exposure-response (IER) model. The estimated PM_2.5-mortality in India revealed an annual increasing rate of 2.7% during the study period. Spatially, deaths due to the exposure to ambient PM_2.5 concentrated mostly in populated North India, and four northern states contributed 43% to the national PM_2.5-attributable deaths in 2015. PM_2.5-attributable deaths in India increased by 21% during 1998-2015 due to the changes of PM_2.5 only, and deaths due to lung cancer (LC) revealed the largest sensitivity to increasing ambient PM_2.5. The findings of this study suggest that aggressive air pollution control strategies should be implemented in North India due to their dominant contributions to the current health risks. Moreover, the rapid growth of LC related deaths with increasing ambient PM_2.5 should not be neglected.

Identification of key aerosol types and mixing states in the central Indian Himalayas during the GVAX campaign: the role of particle size in aerosol classification

Studies in aerosol properties, types and sources in the Himalayas are important for atmospheric and climatic issues due to high aerosol loading in the neighboring plains. This study uses in situ measurements of aerosol optical and microphysical properties obtained during the Ganges Valley Aerosol eXperiment (GVAX) at Nainital, India over the period June 2011-March 2012, aiming to identify key aerosol types and mixing states for two particle sizes (PM₁ and PM₁₀). Using a classification matrix based on SAE vs. AAE thresholds (scattering vs. absorption Ångström exponents, respectively), seven aerosol types are identified, which are highly dependent on particle size. An aerosol type named "large/BC mix" dominates in both PM₁ (45.4%) and PM₁₀ (46.9%) mass, characterized by aged BC mixed with other aerosols, indicating a wide range of particle sizes and mixing states. Small particles with low spectral dependence of the absorption (AAE < 1) account for 31.6% and BC-dominated aerosols for 14.8% in PM₁, while in PM₁₀, a large fraction (39%) corresponds to "large/low-absorbing" aerosols and only 3.9% is characterized as "BC-dominated". The remaining types consist of mixtures of dust and local emissions from biofuel burning and display very small fractions. The main optical properties e.g. spectral scattering, absorption, single scattering albedo, activation ratio, as well as seasonality and dependence on wind speed and direction of identified types are examined, revealing a large influence of air masses originating from the Indo-Gangetic Plains. This indicates that aerosols over the central Himalayas are mostly composed by mixtures of processed and transported polluted plumes from the plains. This is the first study that identifies key aerosol populations in the central Indian Himalayas based on in situ measurements and the results are highly important for aerosol-type inventories, chemical transport models and reducing the uncertainty in aerosol radiative forcing over the third pole.

COVID-19 induced lockdown and decreasing particulate matter (PM10): An empirical investigation of an Asian megacity

The air quality in the cities of developing countries is deteriorating with the proliferation of anthropogenic activities that add pollutant matters in the lower part of the troposphere. Particulate matter with an aerodynamic diameter lower than 10 μm (PM₁₀) is considered one of the direct indicators of air quality in an urban area as it brings health morbidities. The article empirically investigates the role COVID-19 related lockdown has played in bringing down pollution level (PM₁₀) in the megacity of Kolkata. It does so by taking account of PM₁₀ level in three stages - pre, presage and complete-lockdown timelines. The extracted results show a significant declining trend (about 77% vis-a-vis the pre-lockdown period) with 95% of the geographical area under 100 μm/m³ and a strong fit with the station-based records. The feasibility and robustness showed by the remotely sensed data along with other earth observatory information for larger-scale pollution prevalence make its adoption imperative. Simultaneously, it becomes urgent in times of lockdown when the physical mobility of maintenance and research staff to stations is significantly curtailed. The work contributes to study on PM₁₀ by its ability to replicate in examining cities of both the global north and global south.

Changing air pollution scenario during COVID-19: Redefining the hotspot regions over India

The present study investigates the air pollution pattern over India during the COVID-19 lockdown period (24 March-31 May 2020), pre-lockdown (1-23 March 2020) and the same periods from 2019 using Moderate Resolution Imaging Spectroradiometer (MODIS) Terra aerosol optical depth (AOD) with level 2 (10 km × 10 km) and level 3 (1° × 1° gridded) collection 6.1 Dark Target Deep Blue (DT-DB) aerosol product the Tropospheric Monitoring Instrument (TROPOMI) NO₂ and SO₂ data with a spatial resolution of 7 km × 3.5 km. We also use long-term average (2000-2017) of AOD for March-May to identify existing hotspot regions and to compare the variations observed in 2019 and 2020. The aim of the present work is to identify the pollution hotspot regions in India that existed during the lockdown and understanding the future projection scenarios reported by previous studies in light of the present findings. We have incorporated Menn-Kendall trend analysis to understand the AOD trends over India and percentage change in AOD, NO₂ and SO₂ to identify air pollution pattern changes during the lockdown. The results indicate higher air pollution levels over eastern India over the coal-fired power plants clusters. By considering the earlier projected studies, our results suggest that eastern India will have higher levels of air pollution, making it a new hotspot region for air pollution with highest magnitudes.

Source apportionment of carbon monoxide over India: a quantitative analysis using MOZART-4

MOZART-4 chemistry transport model has been used to examine the contribution of carbon monoxide (CO) from different source regions/types by tagging their emissions in model simulations. These simulations are made using tagged tracer approach to estimate the relative contribution of different geographical regions and different emission sources, such as anthropogenic or biomass burning to the CO concentration at the surface, in the planetary boundary layer (PBL), and in the free troposphere (FT) over the Indian sub-continent. The CO budget analyses highlight the significant contribution of the Indian emissions on surface CO and influence of chemical production on the free tropospheric CO concentration. The total CO mixing ratio is estimated as 263 ± 139 parts per billion by volume (ppbv) for surface, 177 ± 71 ppbv for PBL, and 112 ± 14 ppbv for FT. The percentage contributions of primary sources are found to be 80%, 68%, and 53% at the surface, in the PBL, and in the FT, respectively. The sub-regional analysis of India shows that anthropogenic and photochemical processes contribute 41-75% and 15-46% CO, respectively, at the surface. Maximum percentage contribution of anthropogenic CO is observed over Indo-Gangetic Plain and Eastern India (75%). CO contribution from local anthropogenic and biomass burning emissions and transported from other global source regions are analyzed over the Indian region at the surface, in the PBL, and in the FT. The local anthropogenic sources contribute largest to the surface CO over India with 108 ppbv, followed by China with 98 ppbv, Europe with 55 ppbv, North America (NA) with 46 ppbv, and South-east Asia (SEA) and Middle East (ME) with 23 ppbv each. India's PBL (FT) CO is mostly influenced by China's anthropogenic emissions with 12 ppbv (8 ppbv) followed by SEA with 7 ppbv (6 ppbv). Surface biomass burning CO over India (6 ppbv) is much lower than in other regions such as SEA (32 ppbv), Africa (24 ppbv), and South America (11 ppbv). In the PBL (FT), SEA and Africa's BB emissions show major impact on CO over India with 6 ppbv (5 ppbv) and 5 ppbv (4 ppbv), respectively.

Silver linings in the dark clouds of COVID-19: Improvement of air quality over India and Delhi metropolitan area from measurements and WRF-CHIMERE model simulations

The current study examines the impact of the COVID-19 lockdown (25th March until May 17, 2020) period in particulate matter (PM) concentrations and air pollutants (NO_x, SO₂, CO, NH₃, and O₃) at 63 stations located at Delhi, Uttar Pradesh and Haryana states within the Delhi-NCR, India. Large average reductions are recorded between the stations in each state such as PM₁₀ (-46 to -58%), PM_2.5 (-49 to -55%), NO₂ (-27 to -58%), NO (-54% to -59%), CO (-4 to -44%), NH₃ (-2 to -38%), while a slight increase is observed for O₃ (+4 to +6%) during the lockdown period compared to same periods in previous years. Furthermore, PM and air pollutants are significantly reduced during lockdown compared to the respective period in previous years, while a significant increase in pollution levels is observed after the re-opening of economy. The meteorological changes were rather marginal between the examined periods in order to justify such large reductions in pollution levels, which are mostly attributed to traffic-related pollutants (NO_x, CO and road-dust PM). The WRF-CHIMERE model simulations reveal a remarkable reduction in PM_2.5, NO₂ and SO₂ levels over whole Indian subcontinent and mostly over urban areas, due to limitation in emissions from the traffic and industrial sectors. A PM_2.5 reduction of -48% was simulated in Delhi in great consistency with measurements, rendering the model as a powerful tool for simulations of lower pollution levels during lockdown period.

A Global Climatology of Dust Aerosols Based on Satellite Data: Spatial, Seasonal and Inter-Annual Patterns over the Period 2005–2019

A satellite-based algorithm is developed and used to determine the presence of dust aerosols on a global scale. The algorithm uses as input aerosol optical properties from the MOderate Resolution Imaging Spectroradiometer (MODIS)-Aqua Collection 6.1 and Ozone Monitoring Instrument (OMI)-Aura version v003 (OMAER-UV) datasets and identifies the existence of dust aerosols in the atmosphere by applying specific thresholds, which ensure the coarse size and the absorptivity of dust aerosols, on the input optical properties. The utilized aerosol optical properties are the multiwavelength aerosol optical depth (AOD), the Aerosol Absorption Index (AI) and the Ångström Exponent (a). The algorithm operates on a daily basis and at 1° × 1° latitude-longitude spatial resolution for the period 2005–2019 and computes the absolute and relative frequency of the occurrence of dust. The monthly and annual mean frequencies are calculated on a pixel level for each year of the study period, enabling the study of the seasonal as well as the inter-annual variation of dust aerosols’ occurrence all over the globe. Temporal averaging is also applied to the annual values in order to estimate the 15-year climatological mean values. Apart from temporal, a spatial averaging is also applied for the entire globe as well as for specific regions of interest, namely great global deserts and areas of desert dust export. According to the algorithm results, the highest frequencies of dust occurrence (up to 160 days/year) are primarily observed over the western part of North Africa (Sahara), and over the broader area of Bodélé, and secondarily over the Asian Taklamakan desert (140 days/year). For most of the study regions, the maximum frequencies appear in boreal spring and/or summer and the minimum ones in winter or autumn. A clear seasonality of global dust is revealed, with the lowest frequencies in November–December and the highest ones in June. Finally, an increasing trend of global dust frequency of occurrence from 2005 to 2019, equal to 56.2%, is also found. Such an increasing trend is observed over all study regions except for North Middle East, where a slight decreasing trend (−2.4%) is found.

Spatio-temporal variability of near-surface air pollutants at four distinct geographical locations in Andhra Pradesh State of India

India is highly vulnerable to air pollution in the recent decade, especially urban areas with rapidly growing urbanisation and industrialisation. Here, we present spatio-temporal variability of air pollutants at four distinct locations in Andhra Pradesh State of India. The mean concentrations of air pollutants were generally higher at Visakhapatnam site than Amaravati, Rajahmundry, and Tirumala sites. The mean concentration of particulate matter of diameter less than 2.5 μm (PM_2.5) was higher at Visakhapatnam site (48.5 ± 27.3 μg/m³) by a factor of about 1.6 as compared to Tirumala site (29.5 ± 17 μg/m³). On the contrary, the mean concentrations of oxides of nitrogen (NO_x, 70.3 ± 28.1 μg/m³) and ammonia (NH₃, 20.5 ± 9.2 μg/m³) were higher at Tirumala by a factor of about 1.4 and 1.9, respectively, as compared to Visakhapatnam (49 ± 5 μg/m³ and 10.7 ± 5 μg/m³). This was mainly attributed to higher vehicular emissions at Tirumala site. PM_2.5, carbon monoxide (CO), NOx, and sulfur dioxide (SO₂) showed distinct seasonal variation, with higher concentrations in winter followed by post-monsoon, pre-monsoon and monsoon. The Concentration Weighted Trajectory analysis of PM_2.5 based on 5-days backward air mass trajectories showed that all sites experienced northeast air mass flow indicative of the outflow from Indo-Gangetic Plain, particularly in the post-monsoon and winter seasons. The Continuous Wavelet Transform analysis further showed that higher variations in PM_2.5 concentrations occurring at a regular interval from a week to 16 days at both Tirumala and Visakhapatnam sites, while weekly periods are dominant over Amaravati and Rajahmundry sites with 95% significance during post-monsoon and winter seasons. Overall, our results underline heterogeneity in air pollution emission sources and influx of pollutants from distant sources, which would be useful when formulating the policies and mitigation procedures for this region.

Climatological trends in satellite-derived aerosol optical depth over North India and its relationship with crop residue burning: Rural-urban contrast

Columnar Aerosol Optical Depths (AOD) over an urban area (Chandigarh) and a rural area (Khera, Fatehgarh Sahib district) situated in the Indo-Gangetic Plains (IGP) of India were analysed to study their temporal heterogeneity in terms of interannual, seasonal and monthly variations. Over the last few decades, IGP has become one of the global hotspots of air pollution due to the increased anthropogenic activities such as traffic, industries, agricultural waste burning etc. Level-2 AODs (550 nm) were retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard NASA's Terra and Aqua satellites, for a period of 14 years (2005-2018). The climatological mean Terra-MODIS (Aqua-MODIS) AOD over the urban location was ~0.497 ± 0.238 (0.474 ± 0.228), whereas over the rural location it was 0.542 ± 0.269 (0.534 ± 0.282). Linear trend analysis estimated an increase in annual mean Terra-MODIS (Aqua-MODIS) AOD at a rate of ~0.009 (0.013) per year over the urban site; whereas over the rural location the rate of increase was ~0.003 (0.004) per year. Results show that the observed increase is ~1.49% (2.41%) of climatological mean AOD over the urban location for Terra-MODIS (Aqua-MODIS), whereas, over the rural location, it was ~0.50% (0.67%). Using the HYSPLIT trajectory model, it was concluded that, during post-monsoon, the observed high AODs can be related to massive crop residue burning in the IGP region. These AOD trends can also be used to track the regional anthropogenic air-pollution changes. An empirical relation between AOD and PM₁₀ was established, which can be used to estimate PM₁₀ over the urban and rural areas of IGP (using MODIS AODs), complementing the sparse ground-based monitoring. Further, satellite-based air pollution data can be used for baseline assessment and understanding the impact of control policies such as National Clean Air Programme and to support formulate evidence-based pollution control strategies.

A Satellite-Based High-Resolution (1-km) Ambient PM2.5 Database for India over Two Decades (2000–2019): Applications for Air Quality Management

Fine particulate matter (PM2.5) is a major criteria pollutant affecting the environment, health and climate. In India where ground-based measurements of PM2.5 is scarce, it is important to have a long-term database at a high spatial resolution for an efficient air quality management plan. Here we develop and present a high-resolution (1-km) ambient PM2.5 database spanning two decades (2000–2019) for India. We convert aerosol optical depth from Moderate Resolution Imaging Spectroradiometer (MODIS) retrieved by Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm to surface PM2.5 using a dynamic scaling factor from Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) data. The satellite-derived daily (24-h average) and annual PM2.5 show a R2 of 0.8 and 0.97 and root mean square error of 25.7 and 7.2 μg/m3, respectively against surface measurements from the Central Pollution Control Board India network. Population-weighted 20-year averaged PM2.5 over India is 57.3 μg/m3 (5–95 percentile ranges: 16.8–86.9) with a larger increase observed in the present decade (2010–2019) than in the previous decade (2000 to 2009). Poor air quality across the urban–rural transact suggests that this is a regional scale problem, a fact that is often neglected. The database is freely disseminated through a web portal ‘satellite-based application for air quality monitoring and management at a national scale’ (SAANS) for air quality management, epidemiological research and mass awareness.

Evaluation of spatial and temporal heterogeneity of black carbon aerosol mass concentration over India using three year measurements from IMD BC observation network

Extensive measurements of equivalent black carbon (EBC) aerosol mass concentration at fifteen stations of India Meteorological Department (IMD) BC observation network during the period 2016-2018 are used to study the spatial and temporal heterogeneity over India. The sampling sites represent different geographical region of India. Spatial distribution shows higher values of EBC over stations of north India and IGP. Highest annual mean EBC mass concentration during study period was reported at two mega cities New Delhi (13,575 ± 8401 ng/m³) followed by Kolkata (12,082 ± 6850 ng/m³) whereas lowest mean concentration was at Ranichauri (1737 ± 884 ng/m³) followed by Bhuj (2021 ± 1471 ng/m³). Stations located in coastal region of south India reported low concentration of EBC. In order to find out the quantitative contribution of biomass burning (EBC_BB) and fossil fuel (EBC_FF) in total mass concentration of EBC, source apportionment study has been carried out using Aethalometer model. The EBC_FF is the dominant contributor to EBC mass concentration at all the sites in every season, while the highest seasonal biomass burning mass contribution (37%) was observed in the winter at a background site Ranichauri. Maximum concentration of EBC_BB was observed at Srinagar (2671 ng/m³) where as EBC_FF was maximum in Delhi (11,074 ng/m³). Seasonal and diurnal variation studies have also been carried out for all the stations. The EBC mass concentrations exhibited strong seasonality, with the highest values occurring in postmonsoon/winter and the lowest in monsoon season. The higher EBC concentration in postmonsoon/winter seasons was attributed to the increased use of fuel in seasonal emission sources, domestic heating and stagnant meteorological conditions, whereas the low levels in monsoon season were related to the precipitation scavenging. Maximum concentration of EBC (22,409 ± 10,510 ng/m³) was observed in winter season over Kolkata. Our study finds high spatial heterogeneity in EBC concentrations across the study area.

Optical and Physical Characteristics of Aerosol Vertical Layers over Northeastern China

The optical and physical characteristics of the aerosol vertical layers over Northeastern China (NEC) are investigated using the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Level 2 layer products from 2007 to 2014. To better examine the spatial and temporal variations in the characteristics of aerosols over NEC, the region is divided into three parts (Heilongjiang province, Jilin province, and Liaoning province) to analyze the inter-annual and seasonal variations of nine selected aerosol parameters in each part during night and day times. The results reveal that the values of aerosol optical depth (AOD) increase year by year, over the whole NEC, being relatively high over the Liaoning (LN) province; this might be induced by higher levels of economic development and agricultural activity. The highest AOD values appear in summer, which is plausibly related to the temperate monsoon climate in NEC. Higher AOD values exist during the daytime than at night; this is intuitively the result of higher daytime anthropogenic activities. The base altitude of the lowest aerosol layer (BAL) and the top altitude of the highest aerosol layer (TAH) varied significantly due to the topography of NEC. The number of aerosol layers (N) is relatively large over LN, which might be caused by a relatively stronger atmospheric convection over this landscape. The thickness of the lowest aerosol layer (TLL) bore little relationship with the topography of NEC. The AOD proportion of the lowest aerosol layer (PAODL) is high (0.70 to 0.85 for the entire NEC), indicating that aerosols are mainly concentrated in the lowest layer of the atmosphere. The volume depolarization ratio of the lowest aerosol layer (VDRL) is large during spring and winter due to the presence of dust aerosols. The color ratio of the lowest aerosol layer (CRL) is large during the day due to relatively more human activities taking place than at night. Moreover, there is a significantly positive linear correlation between N and TAH, and a negative logarithm correlation between N and PAODL over NEC. The results of this study could provide researchers and the government departments with detailed and certain optical and physical information about aerosol layers over NEC, to help in the treatment of air pollution over NEC.

Underlying causes of PM2.5-induced premature mortality and potential health benefits of air pollution control in South and Southeast Asia from 1999 to 2014

Quantification of spatial and temporal variations in premature mortality attributable to PM_2.5 has important implications for air quality control in South and Southeast Asia (SSEA). The number of PM_2.5-induced premature deaths during 1999-2014 in SSEA was estimated using an integrated exposure-response model based on 0.01° × 0.01° satellite-retrieved PM_2.5 data, population density, and spatially and temporally variable baseline mortality data. The results showed extremely high premature death rates in North India and Bangladesh. PM_2.5-induced premature deaths in SSEA increased with small interannual variations from 1999 to 2014 owing to the interannual variations in PM_2.5 concentrations. Moreover, four scenarios on the effects of premature deaths by PM_2.5 mitigation efforts based on World Health Organization (WHO) air quality guidelines (AQG) and interim targets (ITs) were investigated for each disease and each country during 1999-2014. Four scenarios based on WHO AQG (10 μg/m³), IT-3 (15 μg/m³), IT-2 (25 μg/m³), and IT-1 (35 μg/m³) resulted in 69.3%, 49.1%, 25.4%, and 12.8% reductions compared to the total reference premature deaths (1256,300), which was calculated using the original PM_2.5 datasets. Overall, stroke was the most serious disease associated with air pollution, causing 40% of total premature deaths. Ischemic heart disease was the largest contributor (58%) to the deaths in relatively cleaner air (Scenario 1). The annual rate of change in premature deaths in South Asian countries (India, Bangladesh, and Pakistan) was higher than that in Southeast Asian countries under all scenarios. The results for different scenarios provide insight into the largest health benefits of PM_2.5 reduction efforts.

Aerosol columnar characteristics and their heterogeneous nature over Varanasi, in the central Ganges valley

The Indo-Gangetic Basin (IGB) experiences one of the highest aerosol loading over the globe with pronounced inter-/intra-seasonal variability. Four-year (January 2011-December 2014) continuous MICROTOPS-II sun-photometer measurements at Varanasi, central Ganges valley, provide an opportunity to investigate the aerosol physical and optical properties and their variability. A large variation in aerosol optical depth (AOD: from 0.23 to 1.89, mean of 0.82 ± 0.31) and Ångström exponent (AE: from 0.19 to 1.44, mean of 0.96 ± 0.27) is observed, indicating a highly turbid atmospheric environment with significant heterogeneity in aerosol sources, types and optical properties. The highest seasonal means of both AOD and AE are observed in the post-monsoon (October-November) season (0.95 ± 0.31 for AOD and 1.16 ± 0.14 for AE) followed by winter (December, January, February; 0.97 ± 0.34 for AOD and 1.09 ± 0.20 for AE) and are mainly attributed to the accumulation of aerosols from urban and biomass/crop residue burning emissions within a shallow boundary layer. In contrast, during the pre-monsoon and monsoon seasons, the aerosols are mostly coming from natural origin (desert and mineral dust) mixed with pollution in several cases. The spectral dependence of AE, the aerosol "curvature" effect and other graphical techniques are used for the identification of the aerosol types and their mixing processes in the atmosphere. Furthermore, the aerosol source-apportionment assessment using the weighted potential source contribution function (WPSCF) analysis reveals the different aerosol types, emission sources and transport pathways.

Using Multi-Angle Imaging SpectroRadiometer Aerosol Mixture Properties for Air Quality Assessment in Mongolia

Ulaanbaatar (UB), the capital city of Mongolia, has extremely poor wintertime air quality with fine particulate matter concentrations frequently exceeding 500 μg/m3, over 20 times the daily maximum guideline set by the World Health Organization. Intensive use of sulfur-rich coal for heating and cooking coupled with an atmospheric inversion amplified by the mid-continental Siberian anticyclone drive these high levels of air pollution. Ground-based air quality monitoring in Mongolia is sparse, making use of satellite observations of aerosol optical depth (AOD) instrumental for characterizing air pollution in the region. We harnessed data from the Multi-angle Imaging SpectroRadiometer (MISR) Version 23 (V23) aerosol product, which provides total column AOD and component-particle optical properties for 74 different aerosol mixtures at 4.4 km spatial resolution globally. To test the performance of the V23 product over Mongolia, we compared values of MISR AOD with spatially and temporally matched AOD from the Dalanzadgad AERONET site and find good agreement (correlation r = 0.845, and root-mean-square deviation RMSD = 0.071). Over UB, exploratory principal component analysis indicates that the 74 MISR AOD mixture profiles consisted primarily of small, spherical, non-absorbing aerosols in the wintertime, and contributions from medium and large dust particles in the summertime. Comparing several machine learning methods for relating the 74 MISR mixtures to ground-level pollutants, including particulate matter with aerodynamic diameters smaller than 2.5 μm ( PM 2.5 ) and 10 μm ( PM 10 ), as well as sulfur dioxide ( SO 2 ), a proxy for sulfate particles, we find that Support Vector Machine regression consistently has the highest predictive performance with median test R 2 for PM 2.5 , PM 10 , and SO 2 equal to 0.461, 0.063, and 0.508, respectively. These results indicate that the high-dimensional MISR AOD mixture set can provide reliable predictions of air pollution and can distinguish dominant particle types in the UB region.

Seasonal Transition in PM10 Exposure and Associated All-Cause Mortality Risks in India

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 the PM₁₀ database. Aerosol extinction coefficients at the surface are calculated from midvisible 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 the 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 postmonsoon 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 postmonsoon season. Mortality risk decreases by 0.5-1.8% in most parts of India in the premonsoon 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.

Long-term trends and spatial patterns of PM2.5-induced premature mortality in South and Southeast Asia from 1999 to 2014

Fine particulate matter (PM_2.5) poses a potential threat to human health, including premature mortality under long-term exposure. Based on a long-term series of high-resolution (0.01°×0.01°) satellite-retrieved PM_2.5 concentrations, this study estimated the premature mortality attributable to PM_2.5 in South and Southeast Asia (SSEA) from 1999 to 2014. Then, the long-term trends and spatial characteristics of PM_2.5-induced premature deaths (1999-2014) were analyzed using trend analyses and standard deviation ellipses. Results showed the estimated number of PM_2.5-induced average annual premature deaths in SSEA was 1,447,000. The numbers increased from 1,179,400 in 1999 to 1,724,900 in 2014, with a growth rate of 38% and net increase of 545,500. Stroke and ischemic heart disease were the two principal contributors, accounting for 39% and 35% of the total, respectively. High values were concentrated in North India, Bangladesh, East Pakistan, and some metropolitan areas of Southeast Asia. An estimated 991,600 deaths in India was quantified (i.e., ~69% of the total premature deaths in SSEA). The long-term trends (1999-2014) of PM_2.5-related premature mortality exhibited consistent incremental tendencies in all countries except Sri Lanka. The findings of this study suggest that strict controls of PM_2.5 concentrations in SSEA are urgently required.

Long-term trends and spatial patterns of satellite-retrieved PM2.5 concentrations in South and Southeast Asia from 1999 to 2014

Fine particulate matter, or PM_2.5, is a serious air pollutant and has significant effects on human health, including premature death. Based on a long-term series of satellite-retrieved PM_2.5 concentrations, this study analyzed the spatial and temporal characteristics of PM_2.5 in South and Southeast Asia (SSEA) from 1999 to 2014 using standard deviation ellipse and trend analyses. A health risk assessment of human exposure to PM_2.5 between 1999 and 2014 was then undertaken. The results show that PM_2.5 concentrations increased in most areas of SSEA from 1999 to 2014 and exceeded the World Health Organization average annual limit of primary PM_2.5 standards. Bangladesh, Pakistan and India experienced average PM_2.5 values higher than the total average for SSEA. From 1999 to 2014, the entirety of SSEA exhibited an increased rate of 0.02μg/m³/year on average. Bangladesh and Myanmar witnessed greater incremental rates of PM_2.5 than India. Correspondingly, the center of the average regional PM_2.5 concentration gradually shifted to the southeast during the study period. The proportion of areas with PM_2.5 concentrations exceeding 35μg/m³ increased consistently, and the areas with PM_2.5 concentrations below 15μg/m³ decreased continuously. The proportion of the population exposed to high PM_2.5 (above 35μg/m³) increased annually. The extent of high-health-risk areas in SSEA expanded in size and extent between 1999 and 2014, particularly in North India, Bangladesh and East Pakistan. Therefore, all of SSEA should receive special attention, and strict controls on PM_2.5 concentrations in SSEA countries are urgently required.

First results from light scattering enhancement factor over central Indian Himalayas during GVAX campaign

The present work examines the influence of relative humidity (RH), physical and optical aerosol properties on the light-scattering enhancement factor [f(RH=85%)] over central Indian Himalayas during the Ganges Valley Aerosol Experiment (GVAX). The aerosol hygroscopic properties were measured by means of DoE/ARM (US Department of Energy, Atmospheric Radiation Measurement) mobile facility focusing on periods with the regular instrumental operation (November-December 2011). The measured optical properties include aerosol light-scattering (σ_sp) and absorption (σ_ap) coefficients and the intensive parameters i.e., single scattering albedo (SSA), scattering Ångström exponent (SAE), absorption Ångström exponent (AAE) and light scattering enhancement factor (f(RH)=σ_sp(RH, λ)/σ_sp(RH_dry, λ)). The measurements were separated for sub-micron (<1μm, D_1μm) and particles with diameter<10μm (D_10μm) in order to examine the influence of particle size on f(RH) and enhancement rate (γ). The particle size affects the aerosol hygroscopicity since mean f(RH=85%) of 1.27±0.12 and 1.32±0.14 are found for D_10μm and D_1μm, respectively. These f(RH) values are relatively low suggesting the enhanced presence of soot and carbonaceous particles from biomass burning activities, which is verified via backward air-mass trajectories. Similarly, the light-scattering enhancement rates are ~0.20 and 0.17 for the D_1μm and D_10μm particles, respectively. However, a general tendency for increasing f(RH) and γ is shown for higher σ_sp and σ_ap values indicating the presence of rather aged smoke plumes, coated with industrial aerosols over northern India, with mean SSA, SAE and AAE values of 0.92, 1.00 and 1.15 respectively. On the other hand, a moderate-to-small dependence of f(RH) and γ on SAE, AAE, and SSA was observed for both particle sizes. Furthermore, f(RH) exhibits an increasing tendency with the number of cloud condensation nuclei (N_CCN) indicating larger particle hygroscopicity but without significant dependence on the activation ratio.

Importance of aerosol non-sphericity in estimating aerosol radiative forcing in Indo-Gangetic Basin

Aerosols are usually presumed spherical in shape while estimating the direct radiative forcing (DRF) using observations or in the models. In the Indo-Gangetic Basin (IGB), a regional aerosol hotspot where dust is a major aerosol species and has been observed to be non-spherical in shape, it is important to test the validity of this assumption. We address this issue using measured chemical composition at megacity Delhi, a representative site of the western IGB. Based on the observation, we choose three non-spherical shapes - spheroid, cylinder and chebyshev, and compute their optical properties. Non-spherical dust enhances aerosol extinction coefficient (β_ext) and single scattering albedo (SSA) at visible wavelengths by >0.05km^-1 and >0.04 respectively, while it decreases asymmetry parameter (g) by ~0.1. Accounting non-sphericity leads top-of-the-atmosphere (TOA) dust DRF to more cooling due to enhanced backscattering and increases surface dimming due to enhanced β_ext. Outgoing shortwave flux at TOA increases by up to 3.3% for composite aerosols with non-spherical dust externally mixed with other spherical species. Our results show that while non-sphericity needs to be accounted for, choice of shape may not be important in estimating aerosol DRF in the IGB.

Temporal variability in aerosol characteristics and its radiative properties over Patiala, northwestern part of India: Impact of agricultural biomass burning emissions

A comprehensive measurements of aerosol optical depth (AOD), particulate matter (PM) and black carbon (BC) mass concentrations have been carried out over Patiala, a semi-urban site in northwest India during October 2008 to September 2010. The measured aerosol data was incorporated in an aerosol optical model to estimate various aerosol optical parameters, which were subsequently used for radiative forcing estimation. The measured AOD at 500 nm (AOD₅₀₀) shows a significant seasonal variability, with maximum value of 0.81 during post-monsoon (PoM) and minimum of 0.56 during winter season. The Ångström exponent (α) has higher values (i.e. more fine-mode fraction) during the PoM/winter periods, and lower (i.e. more coarse-mode fraction) during pre-monsoon (PrM). In contrast, turbidity coefficient (β) exhibits an opposite trend to α during the study period. BC mass concentration varies from 2.8 to 13.9 μg m^-3 (mean: 6.5 ± 3.2 μg m^-3) during the entire study period, with higher concentrations during PoM/winter and lower during PrM/monsoon seasons. The average single scattering albedo (SSA at 500 nm) values are 0.70, 0.72, 0.82 and 0.75 during PoM, winter, PrM and monsoon seasons, respectively. However, inter-seasonal and inter-annual variability in measured aerosol parameters are statistically insignificant at Patiala. These results suggest strong changes in emission sources, aerosol composition, meteorological parameters as well as transport of aerosols over the station. Higher values of AOD, α and BC, along with lower SSA during PoM season are attributed to agriculture biomass burning emissions over and around the station. The estimated aerosol radiative forcing within the atmosphere is positive (i.e. warming) during all the seasons with higher values (∼60 Wm^-2) during PoM-08/PoM-09 and lower (∼40 Wm^-2) during winter-09/PrM-10. The present study highlights the role of BC aerosols from agricultural biomass burning emissions during post-monsoon season for atmospheric warming at Patiala.

Aerosol characterization and radiative properties over Kavaratti, a remote island in southern Arabian Sea from the period of observations

Long-term measurements of spectral aerosol optical depth (AOD) using sun/sky radiometer for a period of five years (2009-2014) from the remote island location at Kavaratti (KVT; 10.56°N, 72.64°E) in the southern Arabian sea have been analysed. Climatologically, AODs decrease from October to reach maximum of ~0.6 (at 500nm) in March, followed by a sudden fall towards May. Significant modulations of intra-seasonal timescales over this general pattern are noticed due to the changes in the relative strength of distinctively different sources. The corresponding changes in aerosol inversion parameters reveal the presence of coarse-mode aerosols during spring and fine-mode absorbing aerosols in autumn and winter months. An overall dominance of a mixed type of aerosols (~41%) with maximum in winter (~53%) was found via the AOD₅₀₀ vs. Ångström exponent (α_440-870) relationship, while biomass-burning aerosols or thick urban/industrial plumes contribute to ~19%. Spectral dependence of Ångström exponent and aerosol absorbing properties were used to identify the aerosol types and its modification processes. Based on air mass back trajectory analysis, we revealed that the advection of aerosols from Indian subcontinent and western regions plays a major role in modifying the optical properties of aerosols over the observational site. The shortwave aerosol direct radiative forcing estimated via SBDART model ranges from -11.00Wm^-2 to -7.38Wm^-2, -21.51Wm^-2 to -14.33Wm^-2 and 3.17Wm^-2 and 10.0Wm^-2 at top of atmosphere, surface and within the atmosphere, respectively. This atmospheric forcing translates into heating rate of 0.62-1.04Kday^-1. Furthermore, the vertical profiles of aerosols and heating rate exhibit significant increase in lower (during winter and autumn) and mid troposphere (during spring). This may cause serious climate implications over Kavaratti with further consequences on cloud microphysics and monsoon rainfall.

Declining pre-monsoon dust loading over South Asia: Signature of a changing regional climate

Desert dust over the Indian region during pre-monsoon season is known to strengthen monsoon circulation, by modulating rainfall through the elevated heat pump (EHP) mechanism. In this context, an insight into long term trends of dust loading over this region is of significant importance in understanding monsoon variability. In this study, using long term (2000 to 2015) aerosol measurements from multiple satellites, ground stations and model based reanalysis, we show that dust loading in the atmosphere has decreased by 10 to 20% during the pre-monsoon season with respect to start of this century. Our analysis reveals that this decrease is a result of increasing pre-monsoon rainfall that in turn increases (decreases) wet scavenging (dust emissions) and slowing circulation pattern over the Northwestern part of the sub-continent.

Disability-adjusted life years and economic cost assessment of the health effects related to PM2.5 and PM10 pollution in Mumbai and Delhi, in India from 1991 to 2015

Particulate air pollution is becoming a serious public health concern in urban cities in India due to air pollution-related health effects associated with disability-adjusted life years (DALYs) and economic loss. To obtain the quantitative result of health impact of particulate matter (PM) in most populated Mumbai City and most polluted Delhi City in India, an epidemiology-based exposure-response function has been used to calculate the attributable number of mortality and morbidity cases from 1991 to 2015 in a 5-year interval and the subsequent DALYs, and economic cost is estimated of the health damage based on unit values of the health outcomes. Here, we report the attributable number of mortality due to PM₁₀ in Mumbai and Delhi increased to 32,014 and 48,651 in 2015 compared with 19,291 and 19,716 in year 1995. And annual average mortality due to PM_2.5 in Mumbai and Delhi was 10,880 and 10,900. Premature cerebrovascular disease (CEV), ischemic heart disease (IHD), and chronic obstructive pulmonary disease (COPD) causes are about 35.3, 33.3, and 22.9% of PM_2.5-attributable mortalities. Total DALYs due to PM10 increased from 0.34 million to 0.51 million in Mumbai and 0.34 million to 0.75 million in Delhi from average year 1995 to 2015. Among all health outcomes, mortality and chronic bronchitis shared about 95% of the total DALYs. Due to PM₁₀, the estimated total economic cost at constant price year 2005 US$ increased from 2680.87 million to 4269.60 million for Mumbai City and 2714.10 million to 6394.74 million for Delhi City, from 1995 to 2015, and the total amount accounting about 1.01% of India's gross domestic product (GDP). A crucial presumption is that in 2030, PM₁₀ levels would have to decline by 44% (Mumbai) and 67% (Delhi) absolutely to maintain the same health outcomes in year 2015 levels. The results will help policy makers from pollution control board for further cost-benefit analyses of air pollution management programs in Mumbai and Delhi.

Columnar aerosol characteristics and radiative forcing over the Doon Valley in the Shivalik range of northwestern Himalayas

Spectral aerosol optical depth (AOD) measurements obtained from multi-wavelength radiometer under cloudless conditions over Doon Valley, in the foothills of the western Himalayas, are analysed during the period January 2007 to December 2012. High AOD values of 0.46 ± 0.08 and 0.52 ± 0.1 at 500 nm, along with low values of Ångström exponent (0.49 ± 0.01 and 0.44 ± 0.03) during spring (March-May) and summer (June-August), respectively, suggest a flat AOD spectrum indicative of coarse-mode aerosol abundance compared with winter (December-February) and autumn (September-November), which are mostly dominated by fine aerosols from urban/industrial emissions and biomass burning. The columnar size distributions (CSD) retrieved from the King's inversion of spectral AOD exhibit bimodal size patterns during spring and autumn, while combinations of the power-law and unimodal distributions better simulate the retrieved CSDs during winter and summer. High values of extinction coefficient near the surface (∼0.8-1.0 km^-1 at 532 nm) and a steep decreasing gradient above are observed via CALIPSO profiles in autumn and winter, while spring and summer exhibit elevated aerosol layers between ∼1.5 and 3.5 km due to the presence of dust. The particle depolarisation ratio shows a slight increasing trend with altitude, with higher values in spring and summer indicative of non-spherical particles of dust origin. The aerosol-climate implications are evaluated via the aerosol radiative forcing (ARF), which is estimated via the synergy of OPAC and SBDART models. On the monthly basis, the ARF values range from ∼ -30 to -90 W m^-2 at the surface, while aerosols cause an overall cooling effect at the top of atmosphere (approx. -5 to -15 W m^-2). The atmospheric heating via aerosol absorption results in heating rates of 1.2-1.6 K day^-1 during March-June, which may contribute to changes in monsoon circulation over northern India and the Himalayas.

Statistical variability comparison in MODIS and AERONET derived aerosol optical depth over Indo-Gangetic Plains using time series modeling

A lot of studies in the literature of Aerosol Optical Depth (AOD) done by using Moderate Resolution Imaging Spectroradiometer (MODIS) derived data, but the accuracy of satellite data in comparison to ground data derived from ARrosol Robotic NETwork (AERONET) has been always questionable. So to overcome from this situation, comparative study of a comprehensive ground based and satellite data for the period of 2001-2012 is modeled. The time series model is used for the accurate prediction of AOD and statistical variability is compared to assess the performance of the model in both cases. Root mean square error (RMSE), mean absolute percentage error (MAPE), stationary R-squared, R-squared, maximum absolute percentage error (MAPE), normalized Bayesian information criterion (NBIC) and Ljung-Box methods are used to check the applicability and validity of the developed ARIMA models revealing significant precision in the model performance. It was found that, it is possible to predict the AOD by statistical modeling using time series obtained from past data of MODIS and AERONET as input data. Moreover, the result shows that MODIS data can be formed from AERONET data by adding 0.251627 ± 0.133589 and vice-versa by subtracting. From the forecast available for AODs for the next four years (2013-2017) by using the developed ARIMA model, it is concluded that the forecasted ground AOD has increased trend.

Variability in optical properties of atmospheric aerosols and their frequency distribution over a mega city "New Delhi," India

The role of atmospheric aerosols in climate and climate change is one of the largest uncertainties in understanding the present climate and in capability to predict future climate change. Due to this, the study of optical properties of atmospheric aerosols over a mega city "New Delhi" which is highly polluted and populated were conducted for two years long to see the aerosol loading and its seasonal variability using sun/sky radiometer data. Relatively higher mean aerosol optical depth (AOD) (0.90 ± 0.38) at 500 nm and associated Angstrom exponent (AE) (0.82 ± 0.35) for a pair of wavelength 400-870 nm is observed during the study period indicating highly turbid atmosphere throughout the year. Maximum AOD value is observed in the months of June and November while minimum is in transition months March and September. Apart from this, highest value of AOD (AE) value is observed in the post-monsoon [1.00 ± 0.42 (1.02 ± 0.16)] season followed by the winter [0.95 ± 0.36 (1.02 ± 0.20)] attributed to significance contribution of urban as well as biomass/crop residue burning aerosol which is further confirmed by aerosol type discrimination based on AOD vs AE. During the pre-monsoon season, mostly dust and mixed types aerosols are dominated. AODs value at shorter wavelength observed maximum in June and November while at longer wavelength maximum AOD is observed in June only. For the better understanding of seasonal aerosol modification process, the aerosol curvature effect is studied which show a strong seasonal dependency under a high turbid atmosphere, which are mainly associated with various emission sources. Five days air mass back trajectories were computed. They suggest different patterns of particle transport during the different seasons. Results suggest that mixtures of aerosols are present in the urban environment, which affect the regional air quality as well as climate. The present study will be very much useful to the modeler for validation of satellite data with observed data during estimation of radiative effect.

Cause-specific premature death from ambient PM2.5 exposure in India: Estimate adjusted for baseline mortality

In India, more than a billion population is at risk of exposure to ambient fine particulate matter (PM2.5) concentration exceeding World Health Organization air quality guideline, posing a serious threat to health. Cause-specific premature death from ambient PM2.5 exposure is poorly known for India. Here we develop a non-linear power law (NLP) function to estimate the relative risk associated with ambient PM2.5 exposure using satellite-based PM2.5 concentration (2001-2010) that is bias-corrected against coincident direct measurements. We show that estimate of annual premature death in India is lower by 14.7% (19.2%) using NLP (integrated exposure risk function, IER) for assumption of uniform baseline mortality across India (as considered in the global burden of disease study) relative to the estimate obtained by adjusting for state-specific baseline mortality using GDP as a proxy. 486,100 (811,000) annual premature death in India is estimated using NLP (IER) risk functions after baseline mortality adjustment. 54.5% of premature death estimated using NLP risk function is attributed to chronic obstructive pulmonary disease (COPD), 24.0% to ischemic heart disease (IHD), 18.5% to stroke and the remaining 3.0% to lung cancer (LC). 44,900 (5900-173,300) less premature death is expected annually, if India achieves its present annual air quality target of 40μgm(-3). Our results identify the worst affected districts in terms of ambient PM2.5 exposure and resulting annual premature death and call for initiation of long-term measures through a systematic framework of pollution and health data archive.

Quantifying enhancement in aerosol radiative forcing during 'extreme aerosol days' in summer at Delhi National Capital Region, India

Changes in aerosol characteristics (spectral aerosol optical depth, AOD and composition) are examined during the transition from 'relatively clean' to 'extreme' aerosol days in the summer of 2012 at Delhi National Capital Region (NCR), India. AOD smaller than 0.54 (i.e. 12-year mean AOD-1σ) represents 'relatively clean' days in Delhi during the summer. 'Extreme' days are defined by the condition when AOD0.5 exceeds 12-year mean AOD+1 standard deviation (σ). Mean (±1σ) AOD increases to 1.2±0.12 along with a decrease of Angstrom Exponent from 0.54±0.09 to 0.22±0.12 during the 'extreme' days. Aerosol composition is inferred by fixing the number concentrations of various individual species through iterative tweaking when simulated (following Mie theory) AOD spectrum matches with the measured one. Contribution of coarse mode dust to aerosol mass increased from 76.8% (relatively clean) to 96.8% (extreme events), while the corresponding contributions to AOD0.5 increased from 35.0% to 70.8%. Spectrally increasing single scattering albedo (SSA) and CALIPSO aerosol sub-type information support the dominant presence of dust during the 'extreme' aerosol days. Aerosol direct radiative forcing (ADRF) at the top-of-the-atmosphere increases from 21.2Wm(-2) (relatively clean) to 56.6Wm(-2) (extreme), while the corresponding change in surface ADRF is from -99.5Wm(-2) to -153.5Wm(-2). Coarse mode dust contributes 60.3% of the observed surface ADRF during the 'extreme' days. On the contrary, 0.4% mass fraction of black carbon (BC) translates into 13.1% contribution to AOD0.5 and 33.5% to surface ADRF during the 'extreme' days. The atmospheric heating rate increased by 75.1% from 1.7K/day to 2.96K/day during the 'extreme' days.

Hygroscopic growth of particles nebulized from water-soluble extracts of PM2.5 aerosols over the Bay of Bengal: Influence of heterogeneity in air masses and formation pathways

Hygroscopic properties of water-soluble matter (WSM) extracted from fine-mode aerosols (PM2.5) in the marine atmospheric boundary layer of the Bay of Bengal (BoB) have been investigated during a cruise from 27th December 2008 to 30th January 2009. Hygroscopic growth factors were measured on particles generated from the WSM using an H-TDMA system with an initial dry size of 100 nm in the range of 5-95% relative humidity (RH). The measured hygroscopic growth of WSM at 90% RH, g(90%)WSM, were ranged from 1.11 to 1.74 (mean: 1.43 ± 0.19) over the northern BoB and 1.12 to 1.38 (mean: 1.25 ± 0.09) over the southern BoB. A key finding is that distinct hygroscopic growth factors are associated with the air masses from the Indo-Gangetic plains (IGP), which are clearly distinguishable from those associated with air masses from Southeast Asia (SEA). We found higher (lower) g(90%)WSM over the northern (southern) BoB, which were associated with an IGP (SEA) air masses, probably due the formation of high hygroscopic salts such as (NH4)2SO4. On the other hand, biomass burning influenced SEA air masses confer the low hygroscopic salts such as K2SO4, MgSO4, and organic salts over the southern BoB. Interestingly, mass fractions of water-soluble organic matter (WSOM) showed negative and positive correlations with g(90%)WSM over the northern and southern BoB, respectively, suggesting that the mixing state of organic and inorganic fractions could play a major role on the g(90%)WSM over the BoB. Further, WSOM/SO4(2-) mass ratios suggest that SO4(2-) dominates the g(90%)WSM over the northern BoB whereas WSOM fractions were important over the southern BoB. The present study also suggests that aging process could significantly alter the hygroscopic growth of aerosol particles over the BoB, especially over the southern BoB.