India Is Overtaking China as the World's Largest Emitter of Anthropogenic Sulfur Dioxide

Satellite or ground-based measurements for air pollutants (PM2.5, PM10, SO2, NO2, O3) data and their health hazards: which is most accurate and why?

Air pollution is growing at alarming rates on regional and global levels, with significant consequences for human health, ecosystems, and change in climatic conditions. The present 12 weeks (4 October 2021, to 26 December 2021) study revealed the different ambient air quality parameters, i.e., PM2.5, PM10, SO2, NO2, and O3 over four different sampling stations of Delhi-NCR region (Dwarka, Knowledge park III, Sector 125, and Vivek Vihar), India, by using satellite remote sensing data (MERRA-2, OMI, and Aura Satellite) and different ground-based instruments. The ground-based observation revealed the mean concentration of PM2.5 in Dwarka, Knowledge park III, Sector 125, and Vivek Vihar as 279 µg m-3, 274 µg m-3, 294 µg m-3, and 365 µg m-3, respectively. The ground-based instrumental concentration of PM2.5 was greater than that of satellite observations, while as for SO2 and NO2, the mean concentration of satellite-based monitoring was higher as compared to other contaminants. Negative and positive correlations were observed among particulate matter, trace gases, and various meteorological parameters. The wind direction proved to be one of the prominent parameter to alter the variation of these pollutants. The current study provides a perception into an observable behavior of particulate matter, trace gases, their variation with meteorological parameters, their health hazards, and the gap between the measurements of satellite remote sensing and ground-based measurements.

Modeling based on machine learning to investigate flue gas desulfurization performance by calcium silicate absorbent in a sand bed reactor

Flue gas desulfurization (FGD) is a critical process for reducing sulfur dioxide (SO2) emissions from industrial sources, particularly power plants. This research uses calcium silicate absorbent in combination with machine learning (ML) to predict SO2 concentration within an FGD process. The collected dataset encompasses four input parameters, specifically relative humidity, absorbent weight, temperature, and time, and incorporates one output parameter, which pertains to the concentration of SO2. Six ML models were developed to estimate the output parameters. Statistical metrics such as the coefficient of determination (R2) and mean squared error (MSE) were employed to identify the most suitable model and assess its fitting effectiveness. The random forest (RF) model emerged as the top-performing model, boasting an R2 of 0.9902 and an MSE of 0.0008. The model's predictions aligned closely with experimental results, confirming its high accuracy. The most suitable hyperparameter values for RF model were found to be 74 for n_estimators, 41 for max_depth, false for bootstrap, sqrt for max_features, 1 for min_samples_leaf, absolute_error for criterion, and 3 for min_samples_split. Three-dimensional surface plots were generated to explore the impact of input variables on SO2 concentration. Global sensitivity analysis (GSA) revealed absorbent weight and time significantly influence SO2 concentration. The integration of ML into FGD modeling offers a novel approach to optimizing the efficiency and effectiveness of this environmentally crucial process.

Evaluation of Uncertainties in the Anthropogenic SO2 Emissions in the USA from the OMI Point Source Catalog

Satellite remote sensing is a promising method of monitoring emissions that may be missing in inventories, but the accuracy of these estimates is often not clear. We demonstrate here a comprehensive evaluation of errors in anthropogenic sulfur dioxide (SO₂) emission estimates from NASA's OMI point source catalog for the contiguous US by comparing emissions from the catalog with high-quality emission inventory data over different dimensions including size of individual sources, aggregate vs individual source errors, and potential bias in individual source estimates over time. For sources that are included in the catalog, we find that errors in aggregate (sum of error for all included sources) are relatively low. Errors for individual sources in any given year can be substantial, however, with over- or underestimates in terms of total error ranging from -80 to 110 kt (roughly 10-90th percentile). We find that these errors are not necessarily random over time and that there can be consistently positive or negative biases for individual sources. We did not find any overall statistical relationship between the degree of isolation of a source and bias, either at a 40 or 70 km scales. For a sub-set of sources where inventory emissions over a radius of 70 km around an OMI detection are larger than twice the emissions within 40 km, the OMI value is consistently overestimated. We find, as expected, that emission sources not included in the catalog are the largest aggregate source of difference between the satellite estimates and inventories, especially in more recent years where source emission magnitudes have been decreasing and note that trends in satellite detections do not necessarily track trends in total emissions. We find that the OMI-based SO₂ emissions are accurate in aggregate, when summed over a number of sources, but must be interpreted more cautiously at the individual source level. Similar analyses would be valuable for other satellite emission estimates; however, in many cases, the appropriate high-quality reference data may need to be generated.

Sulfate-associated liquid water amplifies the formation of oxalic acid at a semi-arid tropical location over peninsular India during winter

Aerosol liquid water (ALW) can serve as an aqueous-phase medium for numerous chemical reactions and consequently enhance the formation of secondary aerosols in a highly humid atmosphere. However, the aqueous-phase formation of secondary organic aerosols (SOAs) is not well understood in the Indian regions, particularly in tropical peninsular India. In this study, we collected total suspended particulate samples (n = 30) at a semiarid station (Ballari; 15.15°N, 76.93°E; 495 m asl) in tropical peninsular India during the winter of 2016. Homologous series of dicarboxylic acids (C₂-C₁₂), oxoacids (ωC₂-ωC₉), pyruvic acid (Pyr), and glyoxal (Gly) were determined by employing a water-extraction of aerosol and analyzed using capillary gas chromatography (GC). Results show that oxalic acid (C₂) was the most abundant organic acid, followed by succinic (C₄), malonic (C₃), azelaic (C₉), and glyoxylic (ωC₂) or phthalic (Ph) acids. Total diacids-C accounted for 1.7-5.8 % of water-soluble organic carbon (WSOC) and 0.6-3.6 % of total carbon (TC). ALW, estimated from the ISORROPIA 2.1 model, showed a strong linear relationship with sulfate (SO₄^2-), C₂, C₃, C₄, ωC₂, Pyr, and Gly. Based on molecular distribution, specific mass ratios (C₂/C₃, C₂/C₄, C₂/Gly, and Ph/C₉), linear relationships among the measured organic acids, ALW, organic (levoglucosan and oleic acid), and inorganic (SO₄^2-) marker compounds, we emphasize that diacids and related organic compounds, especially C₂, majorly form via aqueous-phase oxidation of precursor compounds including aromatic hydrocarbons (HCs) and unsaturated fatty acids (FAs) originated from biomass burning and combustion-related sources. The present study demonstrates that sulfate driven ALW largely enhances the formation of SOAs via the aqueous-phase reactions over tropical peninsular India during winter.

Long-term ambient air pollution exposure and cardio-respiratory disease in China: findings from a prospective cohort study

BACKGROUND

Existing evidence on long-term ambient air pollution (AAP) exposure and risk of cardio-respiratory diseases in China is mainly on mortality, and based on area average concentrations from fixed-site monitors for individual exposures. Substantial uncertainty persists, therefore, about the shape and strength of the relationship when assessed using more personalised individual exposure data. We aimed to examine the relationships between AAP exposure and risk of cardio-respiratory diseases using predicted local levels of AAP.

METHODS

A prospective study included 50,407 participants aged 30-79 years from Suzhou, China, with concentrations of nitrogen dioxide (NO2), sulphur dioxide (SO2), fine (PM2.5), and inhalable (PM10) particulate matter, ozone (O3) and carbon monoxide (CO) and incident cases of cardiovascular disease (CVD) (n = 2,563) and respiratory disease (n = 1,764) recorded during 2013-2015. Cox regression models with time-dependent covariates were used to estimate adjusted hazard ratios (HRs) for diseases associated with local-level concentrations of AAP exposure, estimated using Bayesian spatio-temporal modelling.

RESULTS

The study period of 2013-2015 included a total of 135,199 person-years of follow-up for CVD. There was a positive association of AAP, particularly SO2 and O3, with risk of major cardiovascular and respiratory diseases. Each 10 µg/m3 increase in SO2 was associated with adjusted hazard ratios (HRs) of 1.07 (95% CI: 1.02, 1.12) for CVD, 1.25 (1.08, 1.44) for COPD and 1.12 (1.02, 1.23) for pneumonia. Similarly, each 10 µg/m3 increase in O3 was associated with adjusted HR of 1.02 (1.01, 1.03) for CVD, 1.03 (1.02, 1.05) for all stroke, and 1.04 (1.02, 1.06) for pneumonia.

CONCLUSIONS

Among adults in urban China, long-term exposure to ambient air pollution is associated with a higher risk of cardio-respiratory disease.

Chinese industrial air pollution emissions based on the continuous emission monitoring systems network

As the world's largest industrial producer, China has generated large amount of industrial atmospheric pollution, particularly for particulate matter (PM), SO₂ and NO_x emissions. A nationwide, time-varying, and up-to-date air pollutant emission inventory by industrial sources has great significance to understanding industrial emission characteristics. Here, we present a nationwide database of industrial emissions named Chinese Industrial Emissions Database (CIED), using the real smokestack concentrations from China's continuous emission monitoring systems (CEMS) network during 2015-2018 to enhance the estimation accuracy. This hourly, source-level CEMS data enables us to directly estimate industrial emission factors and absolute emissions, avoiding the use of many assumptions and indirect parameters that are common in existing research. The uncertainty analysis of CIED database shows that the uncertainty ranges are quite small, within ±7.2% for emission factors and ±4.0% for emissions, indicating the reliability of our estimates. This dataset provides specific information on smokestack concentrations, emissions factors, activity data and absolute emissions for China's industrial emission sources, which can offer insights into associated scientific studies and future policymaking.

A high-precision aerosol retrieval algorithm for FY-3D MERSI-II images

The Medium Resolution Spectral Imager-II (MERSI-II) onboard the recently launched Chinese Fengyun-3D (FY-3D) satellite has great capability in detecting global aerosols as it includes aerosol bands similar to Moderate Resolution Imaging Spectroradiometer (MODIS). However, to date, aerosol retrieval based on MERSI-II is still limited to dark target regions and there is no official aerosol products for the MERSI-II. This study focuses on developing a high-precision algorithm to retrieve aerosol optical depth (AOD) suitable for entire land areas (except snow/ice and inland waters) based on MERSI-II measurements. Considering both the accuracy and retrieval efficiency, a new cost function is constructed based on (1) the fact that the AOD (550 nm) retrieved independently from different bands should be the same with the correct aerosol model, and (2) the assumption that the aerosol types are the same in the 5 × 5 km spatial range. The retrieval method based on the new cost function is nearly 50 times faster than most current methods using iterative calculations. To extend the application adaption of the FY-3D MERSI-II AOD retrieval and avoid the errors caused by the surface Lambertian hypothesis, a bidirectional reflectance distribution function (BRDF) database is built using MODIS products. Eight candidate aerosol models in different natural zones of China are constructed based on AERONET aerosol products from 2010 - 2021. The new method is applied to MERSI-II images over China and validated against ground-based measurements at 14 stations from 2020 to 2021. MODIS aerosol products from three operational algorithms are also used for comparison purposes. The results show that MERSI-II AOD retrievals agree well with the ground-based measurements with correlation coefficient (R), root mean square error (RMSE), and relative mean bias (RMB) of 0.913, 0.123, and 0.955, respectively. In addition, 72.19 % of AOD matchups fall within the expected error (EE) envelopes. The MERSI-II retrievals show higher accuracy than that of MODIS dark target (DT) and deep blue (DB) products and comparable accuracy of the MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) product. MERSI-II AOD also shows higher stability in terms of spatial and temporal and better performance under heavy aerosol loading conditions than MODIS products. A good AOD agreement with R from 0.777 to 0.863 between MERSI-II and MODIS products is found over the land of China. The new method showing high retrieval efficiency and accuracy has great potential to be operationally applied on AOD retrieval for MERSI-II.

Fixing sulfur dioxide by feeding calcine oxide into the rotary volatilization kiln in zinc smelting plant

Sulfur dioxide (SO₂) is a toxic pollutant and its fixation is a high cost but imperative task for sulfide metallurgy industry. Although being a mature technology for on-line fixation of SO₂ by limestone injection in coal-fired boilers, its application is rarely investigated in the sulfide metallurgy plant. Extending this technology to the metallurgy industry is highly plausible, but with the feasibility and practicability waiting to be uncovered. Herein, feeding CaO into the rotary volatilization kiln as SO₂-fixation agent is demonstrated to be an efficient in-furnace desulfurization strategy for zinc smelting plant. The sulfur distribution within the entire smelting process is systematically analyzed, determining that the critical procedure for pressuring the desulfurization system is the rotary volatilization kiln. The thermodynamics analysis shows that addition of CaO is feasible for SO₂ fixation by forming CaS or restraining the reductive decomposition of SO₄^2-. The industrial tests, including the online monitoring of kiln flue gas and kiln slag analysis, validate the thermodynamics analysis, realizing a 24.6% reduction of SO₂ in the flue gas by converting gaseous SO₂ to solid CaS via feeding 20% CaO. The present study highlights an effective strategy for on-line fixing the SO₂, being a potential way for relieving the desulfurization pressures in zinc sulfide metallurgy plant.

Global Trends of Acidity in Rainfall and Its Impact on Plants and Soil

Due to its deleterious and large-scale effects on the ecosystem and long-range transboundary nature, acid rain has attracted the attention of scientists and policymakers. Acid rain (AR) is a prominent environmental issue that has emerged in the last hundred years. AR refers to any form of precipitation leading to a reduction in pH to less than 5.6. The prime reasons for AR formation encompass the occurrence of sulfur dioxide (SO₂), nitrogen oxides (NO_x), ozone (O₃), and organic acids in air produced by natural as well as anthropogenic activities. India, the top SO₂ emitter, also shows a continuous increase in NO₂ level responsible for AR formation. The plants being immobile unavoidably get exposed to AR which impacts the natural surrounding negatively. Plants get affected directly by AR due to reductions in growth, productivity, and yield by damaging photosynthetic mechanisms and reproductive organs or indirectly by affecting underground components such as soil and root system. Genes that play important role in plant defense under abiotic stress gets also modulated in response to acid rain. AR induces soil acidification, and disturbs the balance of carbon and nitrogen metabolism, litter properties, and microbial and enzymatic activities. This article overviews the factors contributing to AR, and outlines the past and present trends of rainwater pH across the world, and its effects on plants and soil systems.

Subnational implications from climate and air pollution policies in India’s electricity sector

Emissions of greenhouse gases and air pollutants in India are important contributors to climate change and health damages. This study estimates current emissions from India’s electricity sector and simulates the state-level implications of climate change and air pollution policies. We find that (i) a carbon tax results in little short-term emissions reductions because there is not enough dispatchable lower emission spare capacity to substitute coal; (ii) moving toward regional dispatch markets rather than state-level dispatch decisions will not lead to emissions reductions; (iii) policies that have modest emissions effects at the national level nonetheless have disparate state-level emissions impacts; and (iv) pricing or incentive mechanisms tied to production or consumption will result in markedly different costs to states.

Improvements in SO2 pollution in India: role of technology and environmental regulations

India relies heavily on coal-based thermal power plants to meet its energy demands. Sulphur dioxide (SO₂) emitted from these plants and industries is a major air pollutant. Analysis of spatial and temporal changes in SO₂ using accurate and continuous observations is required to formulate mitigation strategies to curb the increasing air pollution in India. Here, we present the temporal changes in SO₂ concentrations over India in the past four decades (1980-2020). Our analysis shows that the Central and East India, and Indo-Gangetic Plain (IGP) are the hotspots of SO₂, as these regions house a cluster of thermal power plants, petroleum refineries, steel manufacturing units, and cement Industries. Thermal power plants (51%), and manufacturing and construction industries (29%) are the main sources of anthropogenic SO₂ in India. Its concentration over India is higher in winter (December-February) and lower in pre-monsoon (March-May) seasons. The temporal analyses reveal that SO₂ concentrations in India increased between 1980 and 2010 due to high coal burning and lack of novel technology to contain the emissions during the period. However, SO₂ shows a decreasing trend in recent decade (2010-2020) because of the environmental regulations and implementation of effective control technologies such as the flue gas desulphurisation (FGD) and scrubber. Since 2010, India's renewable energy production has also been increased substantially when India adopted a sustainable development policy. Therefore, the shift in energy production from conventional coal to renewable sources, solid environmental regulation, better inventory, and effective technology would help to curb SO₂ pollution in India. Both economic growth and air pollution control can be performed hand-in-hand by adopting new technology to reduce SO₂ and GHG emissions.

Air pollution in Delhi, India: It’s status and association with respiratory diseases

The policymakers need research studies indicating the role of different pollutants with morbidity for polluted cities to install a strategic air quality management system. This study critically assessed the air pollution of Delhi for 2016–18 to found out the role of air pollutants in respiratory morbidity under the ICD-10, J00-J99. The critical assessment of Delhi air pollution was done using various approaches. The mean PM_2.5 and PM₁₀ concentrations during the measurement period exceeded both national and international standards by a wide margin. Time series charts indicated the interdependence of PM_2.5 and PM₁₀ and connection with hospital visits due to respiratory diseases. Violin plots showed that daily respiratory disease hospital visits increased during the winter and autumn seasons. The winter season was the worst from the city’s air pollution point of view, as revealed by frequency analyses. The single and multi-pollutant GAM models indicated that short-term exposure to PM₁₀ and SO₂ led to increased hospital visits due to respiratory diseases. Per 10 units increase in concentrations of PM₁₀ brought the highest increase in hospital visits of 0.21% (RR: 1.00, 95% CI: 1.001, 1.002) at lag0-6 days. This study found the robust effect of SO₂ persisted in Delhi from lag0 to lag4 days and lag01 to lag06 days for single and cumulative lag day effects, respectively. While every 10 μg m^-3 increase of SO₂ concentrations on the same day (lag0) led to 32.59% (RR: 1.33, 95% CI: 1.09, 1.61) rise of hospital visits, the cumulative concentration of lag0-1 led to 37.21% (RR: 1.37, 95% CI:1.11, 1.70) rise in hospital visits which further increased to even 83.33% (RR: 1.83, 95% CI:1.35, 2.49) rise at a lag0-6 cumulative concentration in Delhi. The role of SO₂ in inducing respiratory diseases is worrying as India is now the largest anthropogenic SO₂ emitter in the world.

Strategic roadmap to assess forest vulnerability under air pollution and climate change

Although it is an integral part of global change, most of the research addressing the effects of climate change on forests have overlooked the role of environmental pollution. Similarly, most studies investigating the effects of air pollutants on forests have generally neglected the impacts of climate change. We review the current knowledge on combined air pollution and climate change effects on global forest ecosystems and identify several key research priorities as a roadmap for the future. Specifically, we recommend (1) the establishment of much denser array of monitoring sites, particularly in the South Hemisphere; (2) further integration of ground and satellite monitoring; (3) generation of flux-based standards and critical levels taking into account the sensitivity of dominant forest tree species; (4) long-term monitoring of N, S, P cycles and base cations deposition together at global scale; (5) intensification of experimental studies, addressing the combined effects of different abiotic factors on forests by assuring a better representation of taxonomic and functional diversity across the ~73,000 tree species on Earth; (6) more experimental focus on phenomics and genomics; (7) improved knowledge on key processes regulating the dynamics of radionuclides in forest systems; and (8) development of models integrating air pollution and climate change data from long-term monitoring programs.

The costs of "blue sky": environmental regulation and employee income in China

Strict environmental regulations may change the behavioral decisions of firms. Based on the exogenous impact of the Chinese Central Government's inclusion of environmental performance in the assessment targets of municipal officials in 2007, this study uses the difference-in-difference method to explore the impact of environmental regulations on employee income. We find that (1) environmental regulations will significantly reduce the average wage level of employees in polluting industries and have no significant impact in nonpolluting industries. (2) This effect is more pronounced in eastern China, where environmental regulations are more stringent, and in areas where political promotion incentives are stronger. (3) Mechanistic analysis finds that environmental regulations will affect employee income by increasing costs and constraining financing. (4) More importantly, we find that the decline in the average wage level of firms is mainly due to the decline in the average wage level of ordinary employees, and the average wage level of management has not decreased significantly, which means that environmental regulations have expanded the functional income distribution. Our findings contribute to a comprehensive understanding of the effectiveness of environmental regulatory policy implementation and associated economic cost issues.

Trends in the types and absorption characteristics of ambient aerosols over the Indo-Gangetic Plain and North China Plain in last two decades

The sixth assessment report released by the Intergovernmental Panel on Climate Change (IPCC) in 2021 states that our inadequate understanding of magnitudes and trends of atmospheric aerosols, particularly over Asia, is a major source of uncertainty in climate change. In this study, the climatology and trends in different types of aerosols with focus on absorbing aerosols over Kanpur located in the Indo-Gangetic Plain (IGP) in South Asia and Beijing in the North China Plain (NCP) in East Asia are derived for the first time. We perform a first analysis of high-quality time series of columnar aerosols observations over a period of nearly two-decades, along with satellite observations to provide a broader regional perspective. The satellite retrieved aerosol Ångström exponent (AE) values have increased (10-20%) suggesting an increasing contribution of fine aerosols to aerosol optical depth (AOD) over Asia in last 2-decades. Among the three aerosol types [urban-industrial (UI), biomass burning (BB), and dust (DU)], only UI and BB aerosols are present over Kanpur throughout the year, while DU is present along with UI and BB aerosols only during pre-monsoon and monsoon. Overall, there is a positive trend in BB aerosols over both Kanpur and Beijing, a positive (negative) trend in UI aerosols over Kanpur (Beijing), and positive (negative) trend in dust over Beijing (Kanpur). However, only the positive trend in BB aerosol type over Kanpur is statistically significant. Further, among the three absorbing aerosol types [mostly black carbon (MBC), mostly dust (MDU), and mixed (MIX) containing BC and dust], only MBC and MIX are present in post-monsoon and winter over IGP, and MDU is present along with MBC and MIX only during pre-monsoon and monsoon, which is in agreement with aerosol types found. Trends in MBC, MIX and MDU over Kanpur in IGP and in MIX over Beijing are statistically significant. These trends are attributed mainly to the changes in anthropogenic aerosol emissions, and not to natural and climatic factors as their changes are relatively small. These findings on hitherto unavailable climatology and trends in aerosols and absorbing aerosols over two global aerosol hotspots and identified contrasts will be crucial in model simulations to better decipher the aerosol-climate interactions over Asia.

Monsoon climate controls metal loading in global hotspot region of transboundary air pollution

Eastern Asia is a major source of global air pollution. The distribution and intensity of these emissions are becoming well characterized, but their impact on the earth surface considering regional hydroclimatological settings has yet to be quantified. Here we show high-resolution spatiotemporal trace metal distributions of precipitation samples collected throughout the Japanese archipelago in 2013, when the world's coal consumption was the greatest, to depict the mass transportation and deposition of pollution. The results show that metals emitted through coal combustion transported from the continent via prevailing wind were intensively deposited along the western coast of the archipelago during winter due to heavy snowing, resulting in lead (Pb) concentration of precipitations exceed the critical level (> 10 μg l^-1). About 1497 tons of Pb of continental origin loaded through wet deposition accounted for over ca. 87% of the total annual flux in 2013, which constituted ca. 18.5% of the total emissions from China in 2012. This study presents the first detailed picture of monsoon climate-controlled atmospheric metal transportation and loading in the hotspot region after the phase-out of leaded gasoline in the twentieth century. The dataset can serve as a base for evaluating the effect of countermeasures implemented recent year.

Comparison of the Concentration of Suspended Particles and Their Chemical Composition near the Ground Surface and Dust Extinction Coefficient by LIDAR

In many epidemiological studies, the dust extinction coefficient measured by light detection and ranging (LIDAR) is used as an indicator of exposure to Asian dust. However, few reports on the relationship between the distribution of total suspended particles (TSPs) near the ground surface and the dust extinction coefficient exist. In this study, we examined the relationship between the concentrations of TSPs near the ground surface, substances indicative of mineral content, and air pollutants that may be transported with Asian dust and dust extinction coefficients in two regions: Imizu and Yurihama-Matsue, from March to May in 2011 and 2013. In both years, large dust extinction coefficients were observed in Imizu and Matsue on days when the concentrations of TSPs and mineral content indicators were high near the ground surface in Imizu and Yurihama, and Asian dust was expected to be highly suspended. In both regions, the concentrations of TSPs and mineral content indicators were significantly positively correlated with the dust extinction coefficient. The concentrations of all air pollutants analyzed were significantly positively correlated with the dust extinction coefficient in each region in 2013, but not in 2011. These results suggest that the dust extinction coefficient is a useful indicator of Asian dust near the ground surface; however, as harmful air pollutants occasionally move with Asian dust, it is necessary to monitor these pollutants near the ground surface when conducting an epidemiological study on the health effect of airborne particles.

Increasing collaboration between China and India in the environmental sciences to foster global sustainability

As the two largest countries by population, China and India have pervasive effects on the ecosphere. Because of their human population size and long international boundary, they share biodiversity and the threats to it, as well as crops, pests and diseases. We ranked the two countries on a variety of environmental challenges and solutions, illustrating quantitatively their environmental footprint and the parallels between them regarding the threats to their human populations and biodiversity. Yet we show that China and India continue to have few co-authorships in environmental publications, even as their major funding for scientific research has expanded. An agenda for collaboration between China and India can start with the shared Himalaya, linking the countries' scientists and institutions. A broader agenda can then be framed around environmental challenges that have regional patterns. Coordinated and collaborative research has the potential to improve the two countries' environmental performance, with implications for global sustainability.

Effect of COVID-19 Response Policy on Air Quality: A Study in South China Context

Mass suspension of anthropogenic activities is extremely rare, the quarantine due to the coronavirus disease 2019 (COVID-19) represents a natural experiment to investigate the impact of anthropogenic activities on air quality. The mitigation of air pollution during the COVID-19 lockdown has been reported from a global perspective; however, the air pollution levels vary in different regions. This study initiated a novel synthesis of multiple-year satellite observations, national ground measurements towards SO2, NO2 and O3 and meteorological conditions to evaluate the impact of the COVID-19 lockdown in Beihai, a specific city in a less developed area in southwest China, to reveal the potential implications of control strategies for air pollution. The levels of the major air pollutants during the COVID-19 lockdown (LP) and during the same period of previous years (SP) were compared and a series of statistical tools were applied to analyze the sources of air pollution in Beihai. The results show that air pollutant levels decreased with substantial diversity during the LP. Satellite-retrieved NO2 and SO2 levels during the LP decreased by 5.26% and 22.06%, while NO2, SO2, PM2.5 and PM10 from ground measurements during the LP were 25.6%, 2.7%, 22.2% and 22.2% lower than during SP, respectively. Ground measured SO2 concentrations during the LP were only 2.7% lower than during the SP, which may be attributed to uninterrupted essential industrial activities, such as power plants. Polar plots analysis shows that NO2 concentrations were strongly associated with local emission sources, such as automobiles and local industry. Additionally, the much lower levels of NO2 concentrations during the LP and the absence of an evening peak may highlight the significant impact of the traffic sector on NO2. The decrease in daily mean O3 concentrations during the LP may be associated with the reduction in NO2 concentrations. Indications in this study could be beneficial for the formulation of atmospheric protection policies.

Rapid rise in premature mortality due to anthropogenic air pollution in fast-growing tropical cities from 2005 to 2018

Tropical cities are experiencing rapid growth but lack routine air pollution monitoring to develop prescient air quality policies. Here, we conduct targeted sampling of recent (2000s to 2010s) observations of air pollutants from space-based instruments over 46 fast-growing tropical cities. We quantify significant annual increases in nitrogen dioxide (NO₂) (1 to 14%), ammonia (2 to 12%), and reactive volatile organic compounds (1 to 11%) in most cities, driven almost exclusively by emerging anthropogenic sources rather than traditional biomass burning. We estimate annual increases in urban population exposure to air pollutants of 1 to 18% for fine particles (PM_2.5) and 2 to 23% for NO₂ from 2005 to 2018 and attribute 180,000 (95% confidence interval: -230,000 to 590,000) additional premature deaths in 2018 (62% increase relative to 2005) to this increase in exposure. These cities are predicted to reach populations of up to 80 million people by 2100, so regulatory action targeting emerging anthropogenic sources is urgently needed.

Remote sensing study of ozone, NO2, and CO: some contrary effects of SARS-CoV-2 lockdown over India

Escalating emissions of several air pollutants over South Asia could play a detrimental role in the regional and global atmosphere. Therefore, it is necessary to investigate these emissions within the boundary layer and at higher heights utilizing satellite data that are more inclusionary, where limited in situ observations are available. Here, we utilize the Infrared Atmospheric Sounding Interferometer (IASI), Ozone Monitoring Instruments (OMI), TROPOspheric Monitoring Instrument (TROPOMI), and Global Ozone Monitoring Experiment (GOME-2) hyperspectral satellite data to assess the changes in emission sources during Indian lockdown with a primary focus on the tropospheric profiles of ozone and carbon monoxide (CO). A significant reduction (> 20%) in the tropospheric ozone was seen over northern and northeast regions compared to 2018, while a dramatic increase (> 20%) compared to 2019 was seen. The subtropical dynamics mainly contributed to the increased ozone over the northern region. An analysis of the ozone production regime showed mostly NO2 limited regime over the major part of India and VOC limited regime over thermal power plants regions. Unlike in the boundary layer, where CO showed reduction (15-20%), CO profiles showed a consistent increase (as high as 31%) in the free troposphere over the majority of cities and thermal power plants. The CO total column also showed an increase (~ 20%) over central and western India and a slight decrease (5%) over northern India. Similar to CO, an increase (~ 15%) of NO2 column over the western region was observed particularly compared to 2019. However, unlike ozone and CO, reduction of tropospheric NO2 columns was seen over the major part of India, with the highest reduction over northern regions (20-52%). Furthermore, homogeneous yearly differences (> 30%) between OMI and TROPOMI NO2 observations were also seen distinctly over the remote areas. Contrary to surface-based studies, the present study shows an increase in CO, ozone (decrease), and NO2 at several locations and in the free troposphere during the lockdown.

Insights into recent aerosol trends over Asia from observations and CMIP6 simulations

Aerosols continue to contribute the largest uncertainty in climate change. Over Asia, a global aerosol hotspot, spatial patterns of aerosol emissions are changing mainly because of changes in anthropogenic emissions, producing a dipole in atmospheric aerosol loading between East (decrease in emissions) and South Asia (increase in emissions). The resultant aerosol radiative effects are expected to be different as compared to the last decades of the 20^th century because of this emerging Asian aerosol dipole. The projection and assessments of radiative and climate impacts of aerosols rely on simulating accurately the aerosol properties, thus, making it imperative that current climate models involved in climate assessments including the Intergovernmental Panel on Climate Change Assessment Reports, simulate well the magnitude and trends in changing aerosol properties. For the first time, in this study we analyze trends in aerosol properties over Asia from satellite and ground-based observations, and simulations from climate models in Coupled Model Intercomparison Project Phase 6 (CMIP6) experiment with state-of-the-art treatment of aerosol chemistry, physics and meteorology. The results reveal large inter-model differences in model estimates, and discrepancies between model simulations and observations as most models are not able to capture the recent observed magnitudes and trends in aerosol optical depth (AOD) and single scattering albedo (SSA) over Asia. The absolute and the relative (percent) trends (positive and/or negative) in AOD are significantly higher than the trends in SSA. The aerosol-induced effective radiative forcing within the atmosphere simulated with three CMIP6 models show a positive (increasing) trend over Asia. A positive trend in atmospheric heating due to aerosols in model simulations is consistent with model simulated trends in AOD (positive) and SSA (negative). These results on model-observations comparison need to be taken into account while examining the projected/expected future climate impacts due to aerosols, and potential value of various mitigation measures, in particular on regional and decadal climate change in Asia which is largely uncertain. MAIN FINDING: Our analysis of satellite and ground-based observations, and simulations from climate models in CMIP6 experiment with state-of-the-art treatment of aerosol chemistry, physics and meteorology reveal large difference in model calculations, and most models are not able to capture the recent observed trends in aerosol optical depth and single scattering albedo over Asia during 2000-2018.

Sector-Based Top-Down Estimates of NO x , SO2, and CO Emissions in East Asia

Top-down estimates using satellite data provide important information on the sources of air pollutants. We develop a sector-based 4D-Var framework based on the GEOS-Chem adjoint model to address the impacts of co-emissions and chemical interactions on top-down emission estimates. We apply OMI NO₂, OMI SO₂, and MOPITT CO observations to estimate NO _x , SO₂, and CO emissions in East Asia during 2005-2012. Posterior evaluations with surface measurements show reduced normalized mean bias (NMB) by 7% (NO₂)-15% (SO₂) and normalized mean square error (NMSE) by 8% (SO₂)-9% (NO₂) compared to a species-based inversion. This new inversion captures the peak years of Chinese SO₂ (2007) and NO _x (2011) emissions and attributes their drivers to industry and energy activities. The CO peak in 2007 in China is driven by residential and industry emissions. In India, the inversion attributes NO _x and SO₂ trends mostly to energy and CO trend to residential emissions.

Optimization and Evaluation of SO2 Emissions Based on WRF-Chem and 3DVAR Data Assimilation

Emission inventories are important for modeling studies and policy-making, but the traditional “bottom-up” emission inventories are often outdated with a time lag, mainly due to the lack of accurate and timely statistics. In this study, we developed a “top-down” approach to optimize the emission inventory of sulfur dioxide (SO2) using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and a three-dimensional variational (3DVAR) system. The observed hourly surface SO2 concentrations from the China National Environmental Monitoring Center were assimilated and used to estimate the gridded concentration forecast errors of WRF-Chem. The concentration forecast errors were then converted to the emission errors by assuming a linear response from SO2 emission to concentration by grids. To eliminate the effects of modelling errors from aspects other than emissions, a strict data-screening process was conducted. Using the Multi-Resolution Emission Inventory for China (MEIC) 2010 as the a priori emission, the emission inventory for October 2015 over Mainland China was optimized. Two forecast experiments were conducted to evaluate the performance of the SO2 forecast by using the a priori (control experiment) and optimized emissions (optimized emission experiment). The results showed that the forecasts with optimized emissions typically outperformed the forecasts with 2010 a priori emissions in terms of the accuracy of the spatial and temporal distributions. Compared with the control experiment, the bias and root-mean-squared error (RMSE) of the optimized emission experiment decreased by 71.2% and 25.9%, and the correlation coefficients increased by 50.0%. The improvements in Southern China were more significant than those in Northern China. For the Sichuan Basin, Yangtze River Delta, and Pearl River Delta, the bias and RMSEs decreased by 76.4–94.2% and 29.0–45.7%, respectively, and the correlation coefficients increased by 23.5–53.4%. This SO2 emission optimization methodology is computationally cost-effective.

Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty

Annual global satellite-based estimates of fine particulate matter (PM2.5) are widely relied upon for air-quality assessment. Here, we develop and apply a methodology for monthly estimates and uncertainties during the period 1998-2019, which combines satellite retrievals of aerosol optical depth, chemical transport modeling, and ground-based measurements to allow for the characterization of seasonal and episodic exposure, as well as aid air-quality management. Many densely populated regions have their highest PM2.5 concentrations in winter, exceeding summertime concentrations by factors of 1.5-3.0 over Eastern Europe, Western Europe, South Asia, and East Asia. In South Asia, in January, regional population-weighted monthly mean PM2.5 concentrations exceed 90 μg/m3, with local concentrations of approximately 200 μg/m3 for parts of the Indo-Gangetic Plain. In East Asia, monthly mean PM2.5 concentrations have decreased over the period 2010-2019 by 1.6-2.6 μg/m3/year, with decreases beginning 2-3 years earlier in summer than in winter. We find evidence that global-monitored locations tend to be in cleaner regions than global mean PM2.5 exposure, with large measurement gaps in the Global South. Uncertainty estimates exhibit regional consistency with observed differences between ground-based and satellite-derived PM2.5. The evaluation of uncertainty for agglomerated values indicates that hybrid PM2.5 estimates provide precise regional-scale representation, with residual uncertainty inversely proportional to the sample size.

The impact of carbon market pilots on air pollution: evidence from China

As China's economic development enters a transitional stage, the government has put forward higher requirements for efficient air pollution control. Different from the traditional environmental regulation methods, China's carbon market pilots (CMP) can use market mechanism to achieve carbon emission reduction policy goals while also providing more efficient control methods for air pollution. Using the unique daily air pollution data of 324 prefecture-level and above cities in China from December 1, 2013, to December 18, 2017, and matching them with the daily data of carbon allowance trading in each CMP, we employ the time-varying difference-in-differences (DID) method to explore the casual relationship between CMP and air pollution, and its influence mechanism. Our results show that (1) the establishment of CMP can reduce air pollution by at least 4.9%, especially on sulfur dioxide, inhalable particulate matter, fine particles, and carbon monoxide. This conclusion is still robust after we use the instrumental variable (IV) method to deal with endogeneity. (2) Mechanism analysis shows that the reducing effect of CMP on air pollution is achieved mainly through three channels, including energy consumption reducing, technological progressing, and industrial structure upgrading. (3) Since CMP have just been established, government regulation plays a stronger role than the market mechanism in the process of CMP affecting air pollution. However, with the continuous improvement of CMP, the market mechanism has also played an increasingly important dynamic effect.

Identification of NO2 and SO2 Pollution Hotspots and Sources in Jiangsu Province of China

Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are important atmospheric trace gases for determining air quality, human health, climate change, and ecological conditions both regionally and globally. In this study, the Ozone Monitoring Instrument (OMI), total column nitrogen dioxide (NO2), and sulfur dioxide (SO2) were used from 2005 to 2020 to identify pollution hotspots and potential source areas responsible for air pollution in Jiangsu Province. The study investigated the spatiotemporal distribution and variability of NO2 and SO2, the SO2/NO2 ratio, and their trends, and potential source contribution function (PSCF) analysis was performed to identify potential source areas. The spatial distributions showed higher values (>0.60 DU) of annual mean NO2 and SO2 for most cities of Jiangsu Province except for Yancheng City (<0.50 DU). The seasonal analyses showed the highest NO2 and SO2 in winter, followed by spring, autumn, and summer. Coal-fire-based room heating and stable meteorological conditions during the cold season may cause higher NO2 and SO2 in winter. Notably, the occurrence frequency of NO2 and SO2 of >1.2 was highest in winter, which varied between 9.14~32.46% for NO2 and 7.84~21.67% for SO2, indicating a high level of pollution across Jiangsu Province. The high SO2/NO2 ratio (>0.60) indicated that industry is the dominant source, with significant annual and seasonal variations. Trends in NO2 and SO2 were calculated for 2005–2020, 2006–2010 (when China introduced strict air pollution control policies during the 11th Five Year Plan (FYP)), 2011–2015 (during the 12th FYP), and 2013–2017 (the Action Plan of Air Pollution Prevention and Control (APPC-AC)). Annually, decreasing trends in NO2 were more prominent during the 12th FYP period (2011–2015: −0.024~−0.052 DU/year) than in the APPC-AC period (2013–2017: −0.007~−0.043 DU/year) and 2005–2020 (−0.002 to −0.012 DU/year). However, no prevention and control policies for NO2 were included during the 11th FYP period (2006–2010), resulting in an increasing trend in NO2 (0.015 to 0.031) observed throughout the study area. Furthermore, the implementation of China’s strict air pollution control policies caused a larger decrease in SO2 (per year) during the 12th FYP period (−0.002~−0.075 DU/year) than in the 11th FYP period (−0.014~−0.071 DU/year), the APPC-AC period (−0.007~−0.043 DU/year), and 2005–2020 (−0.015~−0.032 DU/year). PSCF analysis indicated that the air quality of Jiangsu Province is mainly influenced by local pollution sources.

Improved Anthropogenic SO2 Retrieval from High-Spatial-Resolution Satellite and its Application during the COVID-19 Pandemic

Sulfur dioxide (SO₂) measured by satellites is widely used to estimate anthropogenic emissions. The Sentinel-5 Precursor (S-5P) operational SO₂ product is overestimated compared to the ground-based multiaxis differential optical absorption spectroscopy (MAX-DOAS) measurements in China and shows an opposite variation to the surface measurements, which limits the application of TROPOspheric monitoring instrument (TROPOMI) products in emissions research. Radiometric calibration, a priori profiles, and fitting windows might cause the overestimation of S-5P operational SO₂ product. Here, we improve the optimal-estimation-based algorithm through several calibration methods. The improved retrieval agrees reasonably well with the ground-based measurements (R > 0.70, bias <13.7%) and has smaller biases (-28.9%) with surface measurements over China and India. It revealed that the SO₂ column in March 2020 decreased by 51.6% compared to March 2019 due to the lockdown for curbing the spread of the COVID-19 pandemic, and there was a decrease of 50% during the lockdown than those after the lockdown, similar to the surface measurement trend, while S-5P operational SO₂ product showed an unrealistic increase of 19%. In India, the improved retrieval identified obvious "hot spots" and observed a 30% decrease of SO₂ columns during the lockdown.

Satellite Monitoring for Air Quality and Health

Data from satellite instruments provide estimates of gas and particle levels relevant to human health, even pollutants invisible to the human eye. However, the successful interpretation of satellite data requires an understanding of how satellites relate to other data sources, as well as factors affecting their application to health challenges. Drawing from the expertise and experience of the 2016-2020 NASA HAQAST (Health and Air Quality Applied Sciences Team), we present a review of satellite data for air quality and health applications. We include a discussion of satellite data for epidemiological studies and health impact assessments, as well as the use of satellite data to evaluate air quality trends, support air quality regulation, characterize smoke from wildfires, and quantify emission sources. The primary advantage of satellite data compared to in situ measurements, e.g., from air quality monitoring stations, is their spatial coverage. Satellite data can reveal where pollution levels are highest around the world, how levels have changed over daily to decadal periods, and where pollutants are transported from urban to global scales. To date, air quality and health applications have primarily utilized satellite observations and satellite-derived products relevant to near-surface particulate matter <2.5 μm in diameter (PM_2.5) and nitrogen dioxide (NO₂). Health and air quality communities have grown increasingly engaged in the use of satellite data, and this trend is expected to continue. From health researchers to air quality managers, and from global applications to community impacts, satellite data are transforming the way air pollution exposure is evaluated.

An ANN experiment on the Indian economy: can the change in pollution generate an increase or decrease in GDP acceleration?

In recent years, the concept of sustainable development has enriched numerous scientific researches. Therefore, the combination of economic growth and the environment has been the subject of numerous econometric and statistical models. They demonstrated that there is a two-way relationship between economic growth and pollution. So, we use data from the World Bank database (1971-2014) to assess the possibility that a change (positive or negative) in pollution in India generates a gross domestic product acceleration. Through a Machine Learning approach via artificial neural network analysis, empirical findings show that a deep neural network can predict the outcome under study. The novelty of this paper is to have determined the results through a model based on a comparison with a highly developed country (Japan). The results obtained show that in a country like India, 76% of the time, a change in pollution evolves into a change in the acceleration of the economic growth.

Characterization of airborne particles and cytotoxicity to a human lung cancer cell line in Guangzhou, China

Air pollution by airborne particles is a serious health problem worldwide. The present study was aimed at investigating the concentrations and composition of total suspended particles (TSPs) and PM_2.5 at various industrial/commercial sites of Guangzhou, a megacity of Southern China. Major and trace elements, ions and carbonaceous fraction were determined and main components were calculated. In addition, in order to assess the potential toxic on the respiratory system of these PM, cytotoxicity of size-fractionated particles (PM_10-5.6, PM_5.6-3.3, PM_3.3-1.1, PM_1.1-0.43) for a human lung cancer cell line (A549) was also investigated. Correlations between PM constituents and toxicity were assessed. Median levels of TSPs and PM_2.5 in industrial/commercial sites were 206 and 57.7 μg/m³, respectively. Nickel, Cu, Mo, Mn, Pb, and Ti were the most abundant metals in TSPs and PM_2.5. Industrial activities and coal combustion were the most important sources of carbonaceous particles in the zone. MTT assays showed that PM_10-5.6 and PM_1.1-0.43 had the highest and the lowest cytotoxicity to A549 cell lines, respectively. Inhalable particles around the manufacturing of metal facilities and formal waste treatment plants showed a high cytotoxicity to A549 cell lines. In general terms, no significant correlations were found between main components of PM and toxicity. However, W showed a significant correlation with cell viability.

Isotopic evidence for acidity-driven enhancement of sulfate formation after SO2 emission control

After the 1980s, atmospheric sulfate reduction is slower than the dramatic reductions in sulfur dioxide (SO₂) emissions. However, a lack of observational evidence has hindered the identification of causal feedback mechanisms. Here, we report an increase in the oxygen isotopic composition of sulfate ([Formula: see text]) in a Greenland ice core, implying an enhanced role of acidity-dependent in-cloud oxidation by ozone (up to 17 to 27%) in sulfate production since the 1960s. A global chemical transport model reproduces the magnitude of the increase in observed [Formula: see text] with a 10 to 15% enhancement in the conversion efficiency from SO₂ to sulfate in Eastern North America and Western Europe. With an expected continued decrease in atmospheric acidity, this feedback will continue in the future and partially hinder air quality improvements.

Simulation study on the indirect effect of sulfate on the summer climate over the eastern China monsoon region

In this study, we designed a sensitivity test using the half number concentration of sulfate in the nucleation calculation process to study the aerosol-cloud interaction (ACI) of sulfate on clouds, precipitation, and monsoon intensity in the summer over the eastern China monsoon region (ECMR) with the National Center for Atmospheric Research Community Atmosphere Model version 5. Numerical experiments show that the ACI of sulfate led to an approximately 30% and 34% increase in the cloud condensation nuclei and cloud droplet number concentrations, respectively. Cloud droplet effective radius below 850 hPa decreased by approximately 4% in the southern ECMR, while the total liquid water path increased by 11%. The change in the indirect radiative forcing due to sulfate at the top of the atmosphere in the ECMR during summer was − 3.74 W·m⁻². The decreased radiative forcing caused a surface cooling of 0.32 K and atmospheric cooling of approximately 0.3 K, as well as a 0.17 hPa increase in sea level pressure. These changes decreased the thermal difference between the land and sea and the gradient of the sea-land pressure, leading to a weakening in the East Asian summer monsoon (EASM) and a decrease in the total precipitation rate in the southern ECMR. The cloud lifetime effect has a relatively weaker contribution to summer precipitation, which is dominated by convection. The results show that the ACI of sulfate was one possible reason for the weakening of the EASM in the late 1970s.

Changing patterns in aerosol vertical distribution over South and East Asia

Changing patterns in aerosol concentrations over the Asian region is well documented with a concurrent increase over India and a marked reduction over China. However, aerosol vertical distribution in the changing climate is not fully understood. By combining long-term satellite observations from MODIS and CALIOP, here we show rapid changes in the aerosol vertical distribution over the South and East Asia covering India and China. A statistically significant decreasing (increasing) trend in the boundary layer (free troposphere) aerosol concentrations is noticed over India. ERA-Interim reanalysis model suggests that this increase in free tropospheric aerosol concentrations are due to the lifting of boundary layer pollutants through an increase in convection (and vertical velocity) in a changing climate. In contrast, a consistent decreasing trend is observed over China irrespective of the altitude. Interestingly, a decreasing trend in Aerosol Optical Depth is observed over the northwest India and we relate this to an observed increase in precipitation leading to increase in the vegetation. It is also found that long-term oscillations like QBO, ENSO and solar cycle significantly affect the aerosol concentrations. Thus, it is prudent to conclude that background meteorology and dynamics play an important role in changing patterns of aerosol vertical distribution.

Air pollution declines during COVID-19 lockdowns mitigate the global health burden

The lockdown response to COVID-19 has resulted in an unprecedented reduction in global economic activity and associated air pollutant levels, especially from a decline in land transportation. We utilized a network of >10,000 air quality stations distributed over 34 countries during lockdown dates up until 15 May 2020 to obtain lockdown related anomalies for nitrogen dioxide, ozone and particulate matter smaller than 2.5 μm in diameter (PM_2.5). Pollutant anomalies were related to short-term health outcomes using empirical exposure-response functions. We estimate that there were a net total of 49,900 (11,000 to 90,000; 95% confidence interval) excess deaths and 89,000 (64,700 to 107,000) pediatric asthma emergency room visits avoided during lockdowns. In China and India alone, the PM_2.5-related avoided excess mortality was 19,600 (15,300 to 24,000) and 30,500 (5700 to 68,000), respectively. While the state of COVID-19 imposed lockdown is not sustainable, these findings illustrate the potential health benefits gained by reducing "business as usual" air pollutant emissions from economic activities primarily through finding alternative transportation solutions.

Aerosol-induced atmospheric heating rate decreases over South and East Asia as a result of changing content and composition

Aerosol emissions from human activities are extensive and changing rapidly over Asia. Model simulations and satellite observations indicate a dipole pattern in aerosol emissions and loading between South Asia and East Asia, two of the most heavily polluted regions of the world. We examine the previously unexplored diverging trends in the existing dipole pattern of aerosols between East and South Asia using the high quality, two-decade long ground-based time series of observations of aerosol properties from the Aerosol Robotic Network (AERONET), from satellites (Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI)), and from model simulations (Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). The data cover the period since 2001 for Kanpur (South Asia) and Beijing (East Asia), two locations taken as being broadly representative of the respective regions. Since 2010 a dipole in aerosol optical depth (AOD) is maintained, but the trend is reversed—the decrease in AOD over Beijing (East Asia) is rapid since 2010, being 17% less in current decade compared to first decade of twenty-first century, while the AOD over South Asia increased by 12% during the same period. Furthermore, we find that the aerosol composition is also changing over time. The single scattering albedo (SSA), a measure of aerosol’s absorption capacity and related to aerosol composition, is slightly higher over Beijing than Kanpur, and has increased from 0.91 in 2002 to 0.93 in 2017 over Beijing and from 0.89 to 0.92 during the same period over Kanpur, confirming that aerosols in this region have on an average become more scattering in nature. These changes have led to a notable decrease in aerosol-induced atmospheric heating rate (HR) over both regions between the two decades, decreasing considerably more over East Asia (− 31%) than over South Asia (− 9%). The annual mean HR is lower now, it is still large (≥ 0.6 K per day), which has significant climate implications. The seasonal trends in AOD, SSA and HR are more pronounced than their respective annual trends over both regions. The seasonal trends are caused mainly by the increase/decrease in anthropogenic aerosol emissions (sulfate, black carbon and organic carbon) while the natural aerosols (dust and sea salt) did not change significantly over South and East Asia during the last two decades. The MERRA-2 model is able to simulate the observed trends in AODs well but not the magnitude, while it also did not simulate the SSA values or trends well. These robust findings based on observations of key aerosol parameters and previously unrecognized diverging trends over South and East Asia need to be accounted for in current state-of-the-art climate models to ensure accurate quantification of the complex and evolving impact of aerosols on the regional climate over Asia.

Long-term (2003-2018) trends in aerosol chemical components at a high-altitude background station in the western North Pacific: Impact of long-range transport from continental Asia

This study examined the long-term trends in chemical components in PM_2.5 (particulate matter with aerodynamic diameter ≤2.5 μm) samples collected at Lulin Atmospheric Background Station (LABS) located on the summit of Mt. Lulin (2862 m above mean sea level) in Taiwan in the western North Pacific during 2003-2018. High ambient concentrations of PM_2.5 and its chemical components were observed during March and April every year. This enhancement was primarily associated with the long-range transport of biomass burning (BB) smoke emissions from Indochina, as revealed from cluster analysis of backward air mass trajectories. The decreasing trends in ambient concentrations of organic carbon (-0.67% yr^-1; p = 0.01), elemental carbon (-0.48% yr^-1; p = 0.18), and non-sea-salt (nss) K⁺ (-0.71% yr^-1; p = 0.04) during 2003-2018 indicated a declining effect of transported BB aerosol over the western North Pacific. These findings were supported by the decreasing trend in levoglucosan (-0.26% yr^-1; p = 0.20) during the period affected by the long-range transport of BB aerosol. However, NO₃^- displayed an increasing trend (0.71% yr^-1; p = 0.003) with considerable enhancement resulting from the air masses transported from the Asian continent. Given that the decreasing trends were for the majority of the chemical components, the columnar aerosol optical depth (AOD) also demonstrated a decreasing trend (-1.04% yr^-1; p = 0.0001) during 2006-2018. Overall decreasing trends in ambient (carbonaceous aerosol and nss-K⁺) as well as columnar (e.g., AOD) aerosol loadings at the LABS may influence the regional climate, which warrants further investigations. This study provides an improved understanding of the long-term trends in PM_2.5 chemical components over the western North Pacific, and the results would be highly useful in model simulations for evaluating the effects of BB transport on an area.

Spatio-temporal variations and trends of major air pollutants in China during 2015-2018

The Chinese government, as a policy response, has continued to invest efforts and resources to implement cost-effective air pollution control technologies and stringent regulation to reduce emissions from the most contributing sectors to protect the environment and public health. The higher density of monitoring stations (> 1600) distributed across China provides a timely opportunity to use them to study in detail the national pollution trends in light of more stringent air pollution control policies. In the present study, air quality datasets comprising hourly concentrations of PM2.5, O3, NO2, and SO2 collected across 1309, 1341, 1289, and 1347 monitoring stations respectively were obtained from the National Environmental Monitoring Centre over 4 years (2015-2018) and trend analysis was performed. Results indicate that the overall annual trends for PM2.5 and SO2 were - 2.9 ± 2.7 and - 3.2 ± 3.2 μg/m3/year, while the winter trends were - 4.8 ± 5.8 and - 6.9 ± 8.4 μg/m3/year respectively across China. The daily maximum 8-h average (DMA8) ozone concentration showed a significant positive trend of 2.4 ± 4.6 μg/m3/year, which was comparatively higher in summer at 4.4 ± 9.0 μg/m3/year. On the other side, NO2 trend is not great in number (- 0.45 ± 2.0 μg/m3/year). Overall, 62.2%, 61.8%, and 20.9% of PM2.5, SO2, and NO2 monitoring stations were associated with a negative trend of ≥ - 2 μg/m3/year. For O3 DMA8 concentrations, 50.7% of the monitoring stations showed a significant positive trend of ≥ 2 μg/m3/year. In light of the Chinese government's increasing impetus on combating air pollution and climate change via new policy regulations, it is important to understand the spatio-temporal distributions and relative contributions of the spectrum of gaseous pollutants to the pollution loads as well as identify changing emission loads across sectors. The results of this study will facilitate the formulation of evidence-based air pollution reduction strategies and policies.

Measurements of Indoor and Outdoor Fine Particulate Matter during the Heating Period in Jinan, in North China: Chemical Composition, Health Risk, and Source Apportionment

Fine particulate matter (PM2.5) was simultaneously collected from the indoor and outdoor environments in urban area of Jinan in North China from November to December 2018 to evaluate the characteristics and sources of indoor PM2.5 pollution. The concentrations of indoor and outdoor PM2.5 were 69.0 ± 50.5 µg m−3 and 128.7 ± 67.9 µg m−3, respectively, much higher than the WHO-established 24-h standards for PM2.5, indicating serious PM2.5 pollution of indoor and outdoor environments in urban Jinan. SO42−, NO3−, NH4+, and organic carbon (OC) were the predominant components, which accounted for more than 60% of the PM2.5 concentration. The total elemental risk values in urban Jinan for the three highly vulnerable groups of population (children (aged 2–6 years and 6–12 years) and older adults (≥70 years)) were nearly 1, indicating that exposure to all of the elements in PM2.5 had potential non-carcinogenic risks to human health. Further analyses of the indoor/outdoor concentration ratios, infiltration rates (FINF), and indoor-generated concentration (Cig) indicated that indoor PM2.5 and its major chemical components (SO42−, NO3−, NH4+, OC, and elemental carbon) were primarily determined by outdoor pollution. The lower indoor NO3−/SO42− ratio and FINF of NO3− relative to the outdoor values were due to the volatility of NO3−. Positive matrix factorization (PMF) was performed to estimate the sources of PM2.5 using the combined datasets of indoor and outdoor environments and revealed that secondary aerosols, dust, cement production, and coal combustion/metal smelting were the major sources during the sampling period.

COVID-19 lockdowns cause global air pollution declines

The lockdown response to coronavirus disease 2019 (COVID-19) has caused an unprecedented reduction in global economic and transport activity. We test the hypothesis that this has reduced tropospheric and ground-level air pollution concentrations, using satellite data and a network of >10,000 air quality stations. After accounting for the effects of meteorological variability, we find declines in the population-weighted concentration of ground-level nitrogen dioxide (NO₂: 60% with 95% CI 48 to 72%), and fine particulate matter (PM_2.5: 31%; 95% CI: 17 to 45%), with marginal increases in ozone (O₃: 4%; 95% CI: -2 to 10%) in 34 countries during lockdown dates up until 15 May. Except for ozone, satellite measurements of the troposphere indicate much smaller reductions, highlighting the spatial variability of pollutant anomalies attributable to complex NO_x chemistry and long-distance transport of fine particulate matter with a diameter less than 2.5 µm (PM_2.5). By leveraging Google and Apple mobility data, we find empirical evidence for a link between global vehicle transportation declines and the reduction of ambient NO₂ exposure. While the state of global lockdown is not sustainable, these findings allude to the potential for mitigating public health risk by reducing "business as usual" air pollutant emissions from economic activities. Explore trends here: https://nina.earthengine.app/view/lockdown-pollution.

COVID-19 lockdowns cause global air pollution declines

The lockdown response to coronavirus disease 2019 (COVID-19) has caused an unprecedented reduction in global economic and transport activity. We test the hypothesis that this has reduced tropospheric and ground-level air pollution concentrations, using satellite data and a network of >10,000 air quality stations. After accounting for the effects of meteorological variability, we find declines in the population-weighted concentration of ground-level nitrogen dioxide (NO₂: 60% with 95% CI 48 to 72%), and fine particulate matter (PM_2.5: 31%; 95% CI: 17 to 45%), with marginal increases in ozone (O₃: 4%; 95% CI: -2 to 10%) in 34 countries during lockdown dates up until 15 May. Except for ozone, satellite measurements of the troposphere indicate much smaller reductions, highlighting the spatial variability of pollutant anomalies attributable to complex NO_x chemistry and long-distance transport of fine particulate matter with a diameter less than 2.5 µm (PM_2.5). By leveraging Google and Apple mobility data, we find empirical evidence for a link between global vehicle transportation declines and the reduction of ambient NO₂ exposure. While the state of global lockdown is not sustainable, these findings allude to the potential for mitigating public health risk by reducing "business as usual" air pollutant emissions from economic activities. Explore trends here: https://nina.earthengine.app/view/lockdown-pollution.

COVID-19 lockdowns cause global air pollution declines

The lockdown response to coronavirus disease 2019 (COVID-19) has caused an unprecedented reduction in global economic and transport activity. We test the hypothesis that this has reduced tropospheric and ground-level air pollution concentrations, using satellite data and a network of >10,000 air quality stations. After accounting for the effects of meteorological variability, we find declines in the population-weighted concentration of ground-level nitrogen dioxide (NO2: 60% with 95% CI 48 to 72%), and fine particulate matter (PM2.5: 31%; 95% CI: 17 to 45%), with marginal increases in ozone (O3: 4%; 95% CI: -2 to 10%) in 34 countries during lockdown dates up until 15 May. Except for ozone, satellite measurements of the troposphere indicate much smaller reductions, highlighting the spatial variability of pollutant anomalies attributable to complex NOx chemistry and long-distance transport of fine particulate matter with a diameter less than 2.5 µm (PM2.5). By leveraging Google and Apple mobility data, we find empirical evidence for a link between global vehicle transportation declines and the reduction of ambient NO2 exposure. While the state of global lockdown is not sustainable, these findings allude to the potential for mitigating public health risk by reducing "business as usual" air pollutant emissions from economic activities. Explore trends here: https://nina.earthengine.app/view/lockdown-pollution.

COVID-19 lockdowns cause global air pollution declines

The lockdown response to coronavirus disease 2019 (COVID-19) has caused an unprecedented reduction in global economic and transport activity. We test the hypothesis that this has reduced tropospheric and ground-level air pollution concentrations, using satellite data and a network of >10,000 air quality stations. After accounting for the effects of meteorological variability, we find declines in the population-weighted concentration of ground-level nitrogen dioxide (NO₂: 60% with 95% CI 48 to 72%), and fine particulate matter (PM_2.5: 31%; 95% CI: 17 to 45%), with marginal increases in ozone (O₃: 4%; 95% CI: -2 to 10%) in 34 countries during lockdown dates up until 15 May. Except for ozone, satellite measurements of the troposphere indicate much smaller reductions, highlighting the spatial variability of pollutant anomalies attributable to complex NO_x chemistry and long-distance transport of fine particulate matter with a diameter less than 2.5 µm (PM_2.5). By leveraging Google and Apple mobility data, we find empirical evidence for a link between global vehicle transportation declines and the reduction of ambient NO₂ exposure. While the state of global lockdown is not sustainable, these findings allude to the potential for mitigating public health risk by reducing "business as usual" air pollutant emissions from economic activities. Explore trends here: https://nina.earthengine.app/view/lockdown-pollution.

COVID-19 lockdowns cause global air pollution declines

The lockdown response to coronavirus disease 2019 (COVID-19) has caused an unprecedented reduction in global economic and transport activity. We test the hypothesis that this has reduced tropospheric and ground-level air pollution concentrations, using satellite data and a network of >10,000 air quality stations. After accounting for the effects of meteorological variability, we find declines in the population-weighted concentration of ground-level nitrogen dioxide (NO₂: 60% with 95% CI 48 to 72%), and fine particulate matter (PM_2.5: 31%; 95% CI: 17 to 45%), with marginal increases in ozone (O₃: 4%; 95% CI: -2 to 10%) in 34 countries during lockdown dates up until 15 May. Except for ozone, satellite measurements of the troposphere indicate much smaller reductions, highlighting the spatial variability of pollutant anomalies attributable to complex NO_x chemistry and long-distance transport of fine particulate matter with a diameter less than 2.5 µm (PM_2.5). By leveraging Google and Apple mobility data, we find empirical evidence for a link between global vehicle transportation declines and the reduction of ambient NO₂ exposure. While the state of global lockdown is not sustainable, these findings allude to the potential for mitigating public health risk by reducing "business as usual" air pollutant emissions from economic activities. Explore trends here: https://nina.earthengine.app/view/lockdown-pollution.

COVID-19 lockdowns cause global air pollution declines

The lockdown response to coronavirus disease 2019 (COVID-19) has caused an unprecedented reduction in global economic and transport activity. We test the hypothesis that this has reduced tropospheric and ground-level air pollution concentrations, using satellite data and a network of >10,000 air quality stations. After accounting for the effects of meteorological variability, we find declines in the population-weighted concentration of ground-level nitrogen dioxide (NO₂: 60% with 95% CI 48 to 72%), and fine particulate matter (PM_2.5: 31%; 95% CI: 17 to 45%), with marginal increases in ozone (O₃: 4%; 95% CI: -2 to 10%) in 34 countries during lockdown dates up until 15 May. Except for ozone, satellite measurements of the troposphere indicate much smaller reductions, highlighting the spatial variability of pollutant anomalies attributable to complex NO_x chemistry and long-distance transport of fine particulate matter with a diameter less than 2.5 µm (PM_2.5). By leveraging Google and Apple mobility data, we find empirical evidence for a link between global vehicle transportation declines and the reduction of ambient NO₂ exposure. While the state of global lockdown is not sustainable, these findings allude to the potential for mitigating public health risk by reducing "business as usual" air pollutant emissions from economic activities. Explore trends here: https://nina.earthengine.app/view/lockdown-pollution.

A review of aerosol chemistry in Asia: insights from aerosol mass spectrometer measurements

Anthropogenic emissions in Asia have significantly increased during the last two decades; as a result, the induced air pollution and its influences on radiative forcing and public health are becoming increasingly prominent. The Aerodyne Aerosol Mass Spectrometer (AMS) has been widely deployed in Asia for real-time characterization of aerosol chemistry. In this paper, we review the AMS measurements in Asia, mainly in China, Korea, Japan, and India since 2001 and summarize the key results and findings. The mass concentrations of non-refractory submicron aerosol species (NR-PM₁) showed large spatial distributions with high mass loadings occurring in India and north and northwest China (60.2-81.3 μg m^-3), whereas much lower values were observed in Korea, Japan, Singapore and regional background sites (7.5-15.1 μg m^-3). Aerosol composition varied largely in different regions, but was overall dominated by organic aerosols (OA, 32-75%), especially in south and southeast Asia due to the impact of biomass burning. While sulfate and nitrate showed comparable contributions in urban and suburban regions in north China, sulfate dominated inorganic aerosols in south China, Japan and regional background sites. Positive matrix factorization analysis identified multiple OA factors from different sources and processes in different atmospheric environments, e.g., biomass burning OA in south and southeast Asia and agricultural seasons in China, cooking OA in urban areas, and coal combustion in north China. However, secondary OA (SOA) was a ubiquitous and dominant aerosol component in all regions, accounting for 43-78% of OA. The formation of different SOA subtypes associated with photochemical production or aqueous-phase/fog processing was widely investigated. The roles of primary emissions, secondary production, regional transport, and meteorology on severe haze episodes, and different chemical responses of primary and secondary aerosol species to source emission changes and meteorology were also demonstrated. Finally, future prospects of AMS studies on long-term and aircraft measurements, water-soluble OA, the link of OA volatility, oxidation levels, and phase state were discussed.

Delayed emergence of a global temperature response after emission mitigation

A major step towards achieving the goals of the Paris agreement would be a measurable change in the evolution of global warming in response to mitigation of anthropogenic emissions. The inertia and internal variability of the climate system, however, will delay the emergence of a discernible response even to strong, sustained mitigation. Here, we investigate when we could expect a significant change in the evolution of global mean surface temperature after strong mitigation of individual climate forcers. Anthropogenic CO₂ has the highest potential for a rapidly measurable influence, combined with long term benefits, but the required mitigation is very strong. Black Carbon (BC) mitigation could be rapidly discernible, but has a low net gain in the longer term. Methane mitigation combines rapid effects on surface temperature with long term effects. For other gases or aerosols, even fully removing anthropogenic emissions is unlikely to have a discernible impact before mid-century.

Strong mitigation of anthropogenic emissions is necessary, but it is not clear how fast these efforts would lead to temperature changes. Here, the authors find that there is a substantial delay between reductions of emissions and a detectable change in surface temperature for a number of climate forcers.