Ozone monitoring instrument observations of interannual increases in SO2 emissions from Indian coal-fired power plants during 2005-2012

Time trends in the incidence of interstitial lung disease across Brazil, Russia, India, China and South Africa (BRICS) from 1990 to 2019: An age-period-cohort analysis

BACKGROUND AND OBJECTIVE

The global incidence of interstitial lung disease (ILD) has risen over the past few decades. However, few studies have evaluated the status and incidence trends of ILD in Brazil, Russia, India, China and South Africa (BRICS). This study assesses the trends of ILD incidence across the BRICS with an emphasis on ILD changes from 1990 to 2019.

METHODS

Incidence rates were estimated by the data obtained from the Global Burden of Disease Study 2019 (GBD 2019). Age-period-cohort modelling was used to estimate the effects on ILD from 1990 to 2019, and the net drift and local drift were calculated.

RESULTS

In 2019, a total of 11.4 million cases of ILD were reported in the BRICS countries. From 1990 to 2019, the incidence rate of ILD in BRICS showed an upward trend. India consistently reported the highest incidence rate, while China showed the fastest growth rate (107.6%). Russia reported a similar incidence rates for men and women, with a lower age of peak incidence compared to the other four countries. We found the time effect was unfavourable for BRICS in the first decade, especially for Brazil; in China and Brazil, the risk of people born after 1960 has rapidly decreased.

CONCLUSION

ILD shows a rising incidence in BRICS. with the trends varying based on age and other environmental factors. BRICS should strengthen specific public health approaches and policies for different stages and populations.

Satellite NO2 Retrieval Complicated by Aerosol Composition over Global Urban Agglomerations: Seasonal Variations and Long-Term Trends (2001-2018)

Tropospheric nitrogen dioxide (NO2) poses a serious threat to the environmental quality and public health. Satellite NO2 observations have been continuously used to monitor NO2 variations and improve model performances. However, the accuracy of satellite NO2 retrieval depends on the knowledge of aerosol optical properties, in particular for urban agglomerations accompanied by significant changes in aerosol characteristics. In this study, we investigate the impacts of aerosol composition on tropospheric NO2 retrieval for an 18 year global data set from Global Ozone Monitoring Experiment (GOME)-series satellite sensors. With a focus on cloud-free scenes dominated by the presence of aerosols, individual aerosol composition affects the uncertainties of tropospheric NO2 columns through impacts on the aerosol loading amount, relative vertical distribution of aerosol and NO2, aerosol absorption properties, and surface albedo determination. Among aerosol compositions, secondary inorganic aerosol mostly dominates the NO2 uncertainty by up to 43.5% in urban agglomerations, while organic aerosols contribute significantly to the NO2 uncertainty by -8.9 to 37.3% during biomass burning seasons. The possible contrary influences from different aerosol species highlight the importance and complexity of aerosol correction on tropospheric NO2 retrieval and indicate the need for a full picture of aerosol properties. This is of particular importance for interpreting seasonal variations or long-term trends of tropospheric NO2 columns as well as for mitigating ozone and fine particulate matter pollution.

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.

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.

A critical review and prospect of NO2 and SO2 pollution over Asia: Hotspots, trends, and sources

Nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) are two major atmospheric pollutants that significantly threaten human health, the environment, and ecosystems worldwide. Despite this, only some studies have investigated the spatiotemporal hotspots of NO₂ and SO₂, their trends, production, and sources in Asia. Our study presents a literature review covering the production, trends, and sources of NO₂ and SO₂ across Asian countries (e.g., Bangladesh, China, India, Iran, Japan, Pakistan, Malaysia, Kuwait, and Nepal). Based on the findings of the review, NO₂ and SO₂ pollution are increasing due to industrial activity, fossil fuel burning, biomass burning, heavy traffic movement, electricity generation, and power plants. There is significant concern about health risks associated with NO₂ and SO₂ emissions in Bangladesh, China, India, Malaysia, and Iran, as they pay less attention to managing and controlling pollution. Even though the lack of quality datasets and adequate research in most Asian countries further complicates the management and control of NO₂ and SO₂ pollution. This study has NO₂ and SO₂ pollution scenarios, including hotspots, trends, sources, and their influences on Asian countries. This study highlights the existing research gaps and recommends new research on identifying integrated sources, their variations, spatiotemporal trends, emission characteristics, and pollution level. Finally, the present study suggests a framework for controlling and monitoring these two pollutants' emissions.

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.

Analysis of Air and Soil Quality around Thermal Power Plants and Coal Mines of Singrauli Region, India

Singrauli region is known as the energy capital of India, as it generates nearly 21 GW of electricity, supplied to various parts of the northern India. Many coal-based Thermal Power Plants (TPPs) using coal from several nearby coal mines, and numerous industries are set up in this region which has made it as one of the highly polluted regions of India. In the present study, detailed temporal analysis and forecast of carbon dioxide (CO₂), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and methane (CH₄) concentrations retrieved from satellite data have been carried out for the periods 2005-2020. Based on the classical multiplicative model and using linear regression, the maximum concentration of CO₂, NO₂, SO₂, and CH₄ in the year 2025 is found to be 422.59 ppm, 29.28 ppm, 0.23 DU, and 1901.35 ppbv, respectively. Detailed analysis shows that carbon dioxide has a 95% correlation with all other trace gases. We have also carried out the geo-accumulation index for the presence of various contaminants in the soil of this region. The geo-accumulation index shows that soil in and around thermal power plants and coal mines is contaminated by heavy metals. The cumulative index shows that soil around Hindalco industries, Bina coal mines, Khadia coal mines, and coal-based TPPs (Anpara and Vindhayachal) are highly polluted and a threat to human population living in the region.

Current and Future Estimates of Marginal Emission Factors for Indian Power Generation

Emission factors from Indian electricity remain poorly characterized, despite known spatial and temporal variability. Limited publicly available emissions and generation data at sufficient detail make it difficult to understand the consequences of emissions to climate change and air pollution, potentially missing cost-effective policy designs for the world's third largest power grid. We use reduced-form and full-form power dispatch models to quantify current (2017-2018) and future (2030-2031) marginal CO₂, SO₂, NO_X, and PM_2.5 emission factors from Indian power generation. These marginal emissions represent emissions changes due to small changes in demand. For 2017-2018, spatial variability in marginal CO₂ emission factors range 3 orders of magnitude across India's states. There is limited seasonal and intraday variability with coal generation likely to meet changes in demand more than half the time in more than half of the states. Assuming the Government of India approximate 2030 targets, the median marginal CO₂ emission factor across states decreases by approximately a factor of 2, but emission factors still span 3 orders of magnitude across states. Under 2030-2031 assumptions there is greater seasonal and intraday variability by up to factors of two and four, respectively. Estimates provide emission factors to evaluate interventions such as electric vehicles, increased air conditioning, and energy efficiency.

Consequences of pre and post confinement on the atmospheric air pollutants during spread of COVID-19 in India

COVID-19, a severe respiratory syndrome, was diagnosed in Wuhan, China, and in the last week of January 2020, it was reported in India. The drastic speed of spreading of COVID-19 imposed a total lockdown in India for the first time in four stages. This leads to restrictions on transport, industries, coal-based power plants, etc. During these stages of lockdown, a detailed analysis was done to study the effect of confinement on various air pollutants, PM₁₀, PM_2.5, SO₂, CO, NH₃, and NO_x (NO, NO₂) over the thirteen different stations situated at different states in India. The data were compared with pre-confinement duration at different locations in India. During confinement, the air pollutants showed less value when compared with the pre-confinement stage alarming everyone and also the Indian government to bring up rules and regulations for better air quality index so that such pandemics should be reduced.

Green policy under the competitive electricity market: An agent-based model simulation in Shanghai

China has begun a new round of electricity market reform since 2015, aiming to transform the regulated form into a competitive one. Since the generation cost of clean energy is still relatively high compared to coal-fired power plants, the major source of the energy sector in China, the environmental outcomes from market reform would remain uncertain. Therefore, green policies play a critical role during the process of reform and hence would determine whether China can achieve the goal of carbon-neutral in 2060. In light of these concerns, this paper performed a simulation study based on the agent-based model (ABM) to analyze the market performance and environmental conditions after reform. Our results demonstrate that given current cost disadvantages, the market share will flow to coal-fired power plants in the competitive electricity market without the support of green policies, resulting in the increase of CO2, NOX, SO2, and smoke emissions by 4.85 %, -1.7 %, 6.48 %, and 6.86 %. Further scenario analysis shows that to improve such environmental outcomes, policies should focus on pushing the replacement of coal-fired energy, either by renewable or gas energy. The simulation results of green policy scenarios demonstrate significant co-benefits, that carbon and other pollution emissions will decrease simultaneously. Despite the environmental gain from green policies, results of the policy efficiency analysis indicates that the FIT on renewable energy is the least effective, while pollution tax turns to be the most effective. Our results imply that green policies should be well considered when constructing the competitive electricity market in China, to effectively achieve both the national carbon-neutral and local environmental goals.

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.

Assessment of Air Pollution before, during and after the COVID-19 Pandemic Lockdown in Nanjing, China

A unique illness, the coronavirus disease 2019 (COVID-19), emerged in Wuhan, People’s Republic of China, in December 2019. To reduce the spread of the virus, strict lockdown policies and control measures were put in place all over the world. Due to these enforced limitations, a drastic drop in air pollution and an improvement in air quality were observed. The present study used six air pollutants (PM10, PM2.5, SO2, NO2, CO and O3) to observe trends before, during and after the COVID-19 lockdown period in Nanjing, China. The data were divided into six phases: P1–P3, pre-lockdown (1 October–31 December 2019), lockdown (1 January–31 March 2020), after lockdown (1 April–30 June 2020), P4–P6: the same dates as the lockdown but during 2017, 2018 and 2019. The results indicate that compared with the pre-lockdown phase, the PM10 and PM2.5 average concentrations decreased by –27.71% and –5.09%. Compared with the previous three years, 2017–2019, the reductions in PM10 and PM2.5 were –37.99% and –33.56%, respectively. Among other pollutants, concentrations of SO2 (–32.90%), NO2 (–34.66%) and CO (–16.85%) also decreased during the lockdown, while the concentration of O3 increased by approximately 25.45%. Moreover, compared with the pre- and during lockdown phases, PM10, PM2.5 and NO2 showed decreasing trends while SO2, CO and O3 concentrations increased. These findings present a road map for upcoming studies and provide a new path for policymakers to create policies to improve air quality.

Quantifying COVID-19 enforced global changes in atmospheric pollutants using cloud computing based remote sensing

Global lockdowns in response to the COVID-19 pandemic have led to changes in the anthropogenic activities resulting in perceivable air quality improvements. Although several recent studies have analyzed these changes over different regions of the globe, these analyses have been constrained due to the usage of station based data which is mostly limited up to the metropolitan cities. Also the quantifiable changes have been reported only for the developed and developing regions leaving the poor economies (e.g. Africa) due to the shortage of in-situ data. Using a comprehensive set of high spatiotemporal resolution satellites and merged products of air pollutants, we analyze the air quality across the globe and quantify the improvement resulting from the suppressed anthropogenic activity during the lockdowns. In particular, we focus on megacities, capitals and cities with high standards of living to make the quantitative assessment. Our results offer valuable insights into the spatial distribution of changes in the air pollutants due to COVID-19 enforced lockdowns. Statistically significant reductions are observed over megacities with mean reduction by 19.74%, 7.38% and 49.9% in nitrogen dioxide (NO₂), aerosol optical depth (AOD) and PM_2.5 concentrations. Google Earth Engine empowered cloud computing based remote sensing is used and the results provide a testbed for climate sensitivity experiments and validation of chemistry-climate models. Additionally, Google Earth Engine based apps have been developed to visualize the changes in a real-time fashion.

Electric vehicle-attributed environmental injustice: Pollutant transfer into regions with poor traffic accessibility

Electric vehicles (EVs) are promoted in recent years as an effective way in alleviating the air pollution caused by tailpipe emissions. However, the pollutants derived from EVs are unheeded. EVs rely on electricity to provide power, and thus their related pollution is transferred to the power plants, which gives rise to the environmental and health pressure to the adjacent regions. In this paper, the transfer of EV-attributed PM_2.5, SO₂, and NO_x inhalations in China are studied. Then by comparing the inhalations versus traffic accessibility among the impacted municipalities, this study sheds light on the environmental injustice lying in the mismatch between pollutant inhalations and traffic accessibility. The results reveal that compared with Shanghai and Shenzhen, the promotion of EVs in Beijing triggers higher pollutant inhalations to its surrounding municipalities. North China Power Grid undertakes 715.62 g PM_2.5 inhalation in total, which is 2.51 and 3.20 times higher than the East China Power Grid and the China Southern Power Grid, respectively. The number of municipalities with lower traffic accessibility while higher pollutant inhalation is 8,8, and 17 in North China Power Grid, East China Power Grid, and China Southern Power Grid respectively, indicating conspicuous environmental injustice resulted from the promotion of EVs.

Diurnal and temporal changes in air pollution during COVID-19 strict lockdown over different regions of India

Lockdown measures to contain COVID-19 pandemic has resulted in a considerable change in air pollution worldwide. We estimate the temporal and diurnal changes of the six criteria air pollutants, including particulate matter (PM2.5 and PM10) and gaseous pollutants (NO2, O3, CO, and SO2) during lockdown (25th March - 3rd May 2020) across regions of India using the observations from 134 real-time monitoring sites of Central Pollution Control Board (CPCB). Significant reduction in PM2.5, PM10, NO2, and CO has been found in all the regions during the lockdown. SO2 showed mixed behavior, with a slight increase at some sites but a comparatively significant decrease at other locations. O3 also showed a mixed variation with a mild increase in IGP and a decrease in the South. The absolute decrease in PM2.5, PM10, and NO2 was observed during peak morning traffic hours (08-10 Hrs) and late evening (20-24 Hrs), but the percentage reduction is almost constant throughout the day. A significant decrease in day-time O3 has been found over Indo Gangetic plain (IGP) and central India, whereas night-time O3 has increased over IGP due to less O3 loss. The most significant reduction (∼40-60%) was found in PM2.5 and PM10. The highest decrease in PM was found for the north-west and IGP followed by South and central regions. A considerable reduction (∼30-70%) in NO2 was found except for a few sites in the central region. A similar pattern was observed for CO having a ∼20-40% reduction. The reduction observed for PM2.5, PM10, NO2, and enhancement in O3 was proportional to the population density. Delhi's air quality has improved with a significant reduction in primary pollutants, however, an increase in O3 was observed. The changes reported during the lockdown are combined effect of changes in the emissions, meteorology, and atmospheric chemistry that requires detailed investigations.

High sensitivity room temperature sulfur dioxide sensor based on conductive poly(p-phenylene)/ZSM-5 nanocomposite

Recently, there has been growing interests in the development of composite materials as the new alternative gas sensing materials for replacing metal oxide based sensors which require the elevated operating temperature. Herein, we reported the fabrication and testing of new sensing composite materials based on the conductive poly(p-phenylene) (PPP) nanoparticle and zeolites for sulfur dioxide (SO₂) detection at room temperature under the effects of doping, zeolite type, zeolite content, SO₂ concentration as well as interferences and humidity. The relative electrical conductivity response depended critically on the doping agent type, doping ratio, and doping temperature. The addition of porous zeolites into the doped-PPP (dPPP) matrix induced the improvement in selectivity and sensing performances towards SO₂ as it promoted more surface area for SO₂ adsorption and its new synergistic effect with the conductive dPPP, related to the additional conductive polymer doping from the dissolution of the SO₂ in intrazeolitic water as identified and reported here. Among all materials, the dPPP/ZSM-5 composite with perchloric acid (HClO₄) as the doping agent, the doping ratio of 50:1, the doping temperature of 70 °C, and the zeolite content of 30% exhibited the highest relative response of 25.42 towards 500 mg L^-1 SO₂ with good repeatability. This composite provided the SO₂ sensitivity of 0.0483 L mg^-1 with R² of 0.9927 and the limit of detection (LOD) of 5 mg L^-1 as determined from the electrical conductivity signal to noise ratio. The present sensing material is a potential candidate in the practical detection of SO₂ at room temperature.

Global Sulfur Dioxide Emissions and the Driving Forces

The presence of sulfur dioxide (SO₂) in the air is a global concern because of its severe environmental and public health impacts. Recent evidence from satellite observations shows rapid changes in the spatial distribution of global SO₂ emissions, but such features are generally missing in global emission inventories that use a bottom-up method due to the lack of up-to-date information, especially in developing countries. Here, we rely on the latest data available on emission activities, control measures, and emission factors to estimate global SO₂ emissions for the period 1960-2014 on a 0.1° × 0.1° spatial resolution. We design two counterfactual scenarios to isolate the contributions of emission activity growth and control measure deployment on historical SO₂ emission changes. We find that activity growth has been the major factor driving global SO₂ emission changes overall, but control measure deployment is playing an increasingly important role. With effective control measures deployed in developed countries, the predominant emission contributor has shifted from developed countries in the early 1960s (61%) to developing countries at present (83%). Developing countries show divergency in mitigation strategies and thus in SO₂ emission trends. Stringent controls in China are driving the recent decline in global emissions. A further reduction in SO₂ emissions would come from a large number of developing nations that currently lack effective SO₂ emission controls.

Applying Benefit-Cost Analysis to Air Pollution Control in the Indian Power Sector

Air pollution is a persistent and well-established public health problem in India: emissions from coal-fired power plants have been associated with over 80,000 premature deaths in 2015. Premature deaths could rise by four to five times this number by 2050 without additional pollution controls. We site a model 500 MW coal-fired electricity generating unit at eight locations in India and examine the benefits and costs of retrofitting the plant with a flue-gas desulfurization unit to reduce sulfur dioxide emissions. We quantify the mortality benefits associated with the reduction in sulfates (fine particles) and value these benefits using estimates of the value per statistical life transferred to India from high income countries. The net benefits of scrubbing vary widely by location, reflecting differences in the size of the exposed population. They are highest at locations in the densely populated north of India, which are also among the poorest states in the country.

Spatiotemporal distributions of ambient SO2 across China based on satellite retrievals and ground observations: Substantial decrease in human exposure during 2013-2016

Multiyear spatiotemporal distributions of daily ambient sulfur dioxide (SO₂) are essential for evaluating management effectiveness and assessing human health risk. In this study, we estimate the daily SO₂ levels across China on 0.1^o grid from 2013 to 2016 by assimilating satellite- and ground-based SO₂ observations using the random-forest spatiotemporal kriging (RF-STK) model. The cross-validation R² is 0.64 and 0.81 for predicting the daily and multiyear averages, respectively. The multiyear population-weighted average of SO₂ for China is 28.1 ± 14.0 μg/m³, and the severest SO₂ pollution occurs in the northern China (45.1 ± 14.7 μg/m³). The SO₂ concentration shows a strong seasonality, i.e., highest in winter (41.6 ± 26.4 μg/m³) and lowest in summer (19.6 ± 8.3 μg/m³). During 2013-2016, the annual SO₂ decreases from 34.4 ± 18.2 to 22.7 ± 11.1 μg/m³, and the population% exposed for more than 100 nonattainment days (SO₂ > 20 μg/m³) drops from 86% to 48%. While the seasonality of SO₂ is mainly determined by the meteorological variation, the substantial decrease attributes to the reduced emissions such as from coal consumption. The effectiveness of SO₂ emission reduction varies widely in different prefectures of China. In Shandong province, the SO₂ concentration decreases by -45% while the coal consumption increases by 9%. In Shanxi province, the SO₂ concentration decreases by -15% while the coal consumption decreases by -3%. The contrasting effectiveness between these two provinces is associated with the much fewer waste gas disposal facilities in Shanxi than Shandong. Stricter regulation is required to further lower the SO₂ concentration in order to protect the public health, especially in the northern China.

Use of dispersion model and satellite SO2 retrievals for environmental impact assessment of coal-fired power plants

The aim of this study is to investigate the impact of ten proposed plants along with three operating plants in Çanakkale province of Turkey where the proposed plants are within very close proximity. The province has the highest capacity of the planned plants and the region is also of interest due to its history, tourism and agriculture potential. Current SO₂ pollution was assessed using ground observations and satellite retrievals where the impact of plants was better captured by satellite retrievals. Individual and cumulative impact from proposed and operating plants was simulated by CALPUFF for 2014. The study domain was 150 × 150 km², with 1 × 1 km² cell size. The effect of changing meteorological inputs and domain size were investigated with simulations. Three cases were performed using meteorological inputs: from one surface and one radiosonde station (Case 1), 22 surface and one radiosonde station (Case 2), and 22 surface and two radiosonde stations (Case 3). Case 2 and 3 resulted in higher concentrations and showed larger affected regions than case 1 in all simulations. The cumulative impact of proposed plants indicated national annual and daily limit values were exceeded in Case 2 and 3. Hourly limit values were exceeded in all three cases. Simulations for two selected proposed plants were assessed for plant impact area given in environmental impact area reports. Results indicated the plant impact areas cannot be sufficient to determine the maximum SO₂ concentrations in some cases and using single meteorology station data cannot represent the study area, especially regions with complex terrain and land-sea interactions such as Çanakkale province. Cumulative impact can be underestimated due to small size of plant impact areas not including other plants. Lastly satellite retrievals are better capturing the pollution than air quality monitoring stations which are strongly affected by meteorology.

Hybrid Mass Balance/4D-Var Joint Inversion of NO x and SO2 Emissions in East Asia

Accurate estimates of NO x and SO2 emissions are important for air quality modeling and management. To incorporate chemical interactions of the two species in emission estimates, we develop a joint hybrid inversion framework to estimate their emissions in China and India (2005-2012). Pseudo observation tests and posterior evaluation with surface measurements demonstrate that joint assimilation of SO2 and NO2 can provide more accurate constraints on emissions than single-species inversions. This occurs through synergistic change of O3 and OH concentrations, particularly in conditions where satellite retrievals of the species being optimized have large uncertainties. The percentage changes of joint posterior emissions from the single-species posterior emissions go up to 242% at grid scales, although the national average of monthly emissions, seasonality, and interannual variations are similar. In China and India, the annual budget of joint posterior SO2 emissions is lower, but joint NO x posterior emissions are higher, because NO x emissions increase to increase SO2 concentration and better match Ozone Monitoring Instrument SO2 observations in high-NO x regions. Joint SO2 posterior emissions decrease by 16.5% from 2008 to 2012, while NO x posterior emissions increase by 24.9% from 2005 to 2011 in China-trends which are consistent with the MEIC inventory. Joint NO x and SO2 posterior emissions in India increase by 15.9% and 19.2% from 2005 to 2012, smaller than the 59.9% and 76.2% growth rate using anthropogenic emissions from EDGARv4.3.2. This work shows the benefit and limitation of joint assimilation in emission estimates and provides an efficient framework to perform the inversion.

Satellite-Derived Correlation of SO2, NO2, and Aerosol Optical Depth with Meteorological Conditions over East Asia from 2005 to 2015

Intense economic and industrial development in China has been accompanied by severe local air pollution, as well as in other downwind countries in East Asia. This study analyzes satellite observational data of sulfur dioxide (SO2), nitrogen dioxide (NO2), and aerosol optical depth (AOD) to explore the spatial distribution, long-term temporal variation, and correlation to meteorological conditions over this region over the period 2005–2015. SO2 and NO2 data are retrieved from the ozone monitoring instrument (OMI) onboard the National Aeronautics and Space Administration (NASA) Aura satellite, while AOD data are from the moderate-resolution imaging spectroradiometer (MODIS) onboard the NASA Aqua satellite. Spatial distributions of SO2, NO2, and AOD show the highest levels in the North China Plain (NCP), with hotspots also in Southeastern China (SC) and the Sichuan Basin (SB). Biomass burning also contributes to a high level of AOD in Southeast Asia in spring and in Equatorial Asia in fall. Considering the correlation of pollutant levels to meteorological conditions, monitoring data show that higher temperature and higher relative humidity (RH) favor the conversion of SO2 and NO2 to sulfate and nitrate aerosol, respectively. The impact of stronger lower tropospheric stability facilitates the accumulation of SO2 and NO2 in NCP and SC. Transport of SO2 and NO2 from intense source regions to relatively clean regions is highly influential over East Asia; such transport from the NCP leads to a considerable increase of pollutants in SC, SB, Taiwan Island (TW), and Taiwan Strait (TWS), particularly in winter. Aerosols generated by biomass burning in Southeast Asia and anthropogenic aerosol in SC are transported to TW and TWS and lead to the increase of AOD, with the highest levels of AOD in SC, TW, and TWS occurring in spring. Precipitation results in the removal of pollutants, especially in highly polluted regions, the effect of which is most significant in winter and spring.

Air pollution characteristics in China during 2015-2016: Spatiotemporal variations and key meteorological factors

With rapid economic development and urbanization, China has suffered from severe and persistent air pollution during the past years. In the work, the hourly data of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃ in all of the prefecture-level cities (336 cities) during 2015-2016 were collected to uncover the spatiotemporal variations and influential factors of these pollutants in China. The average concentrations of PM_2.5, PM₁₀, SO₂, NO₂, and CO decreased by 19.32%, 15.34%, 29.30%, 9.39%, and 8.00% from 2015 to 2016, suggesting the effects of efficient control measurements during this period. On the contrary, the O₃ concentration increased by 4.20% during the same period, which mainly owed to high volatile organic compounds (VOCs) loading. The concentrations of PM_2.5, PM₁₀, SO₂, CO and NO₂ showed the highest and the lowest ones in winter and summer, respectively. However, the O₃ concentration peaked in summer, followed by ones in spring and autumn, and presented the lowest one in winter. All of the pollutants exhibited significantly weekly and diurnal cycle in China. PM_2.5, PM₁₀, SO₂, CO and NO₂ presented the higher concentrations on weekdays than those at weekends, all of which showed the bimodal pattern with two peaks at late night (21:00-22:00) and in morning (9:00-10:00), respectively. However, the O₃ concentration exhibited the highest value around 15:00. The statistical analysis suggested that the PM_2.5, PM₁₀, and SO₂ concentrations were significantly associated with precipitation (Prec), atmosphere temperature (T), and wind speed (WS). The CO and NO₂ concentrations displayed the significant relationship with T, while the O₃ concentration was closely linked to the sunshine duration (Tsun) and relative humidity (RH). T and WS were major factors affecting the accumulation of PM and gaseous pollutants at a national scale. At a spatial scale, Prec and T played the important roles on the PM distribution in Northeast China, and the effect of Prec on CO concentration decreased from Southeast China to Northwest China. The results shown herein provide a scientific insight into the meteorology impacts on air pollution over China.

Global Sources of Fine Particulate Matter: Interpretation of PM2.5 Chemical Composition Observed by SPARTAN using a Global Chemical Transport Model

Exposure to ambient fine particulate matter (PM_2.5) is a leading risk factor for the global burden of disease. However, uncertainty remains about PM_2.5 sources. We use a global chemical transport model (GEOS-Chem) simulation for 2014, constrained by satellite-based estimates of PM_2.5 to interpret globally dispersed PM_2.5 mass and composition measurements from the ground-based surface particulate matter network (SPARTAN). Measured site mean PM_2.5 composition varies substantially for secondary inorganic aerosols (2.4-19.7 μg/m³), mineral dust (1.9-14.7 μg/m³), residual/organic matter (2.1-40.2 μg/m³), and black carbon (1.0-7.3 μg/m³). Interpretation of these measurements with the GEOS-Chem model yields insight into sources affecting each site. Globally, combustion sectors such as residential energy use (7.9 μg/m³), industry (6.5 μg/m³), and power generation (5.6 μg/m³) are leading sources of outdoor global population-weighted PM_2.5 concentrations. Global population-weighted organic mass is driven by the residential energy sector (64%) whereas population-weighted secondary inorganic concentrations arise primarily from industry (33%) and power generation (32%). Simulation-measurement biases for ammonium nitrate and dust identify uncertainty in agricultural and crustal sources. Interpretation of initial PM_2.5 mass and composition measurements from SPARTAN with the GEOS-Chem model constrained by satellite-based PM_2.5 provides insight into sources and processes that influence the global spatial variation in PM_2.5 composition.

A Green Desulfurization Technique: Utilization of Flue Gas SO2 to Produce H2 via a Photoelectrochemical Process Based on Mo-Doped BiVO4

A green photoelectrochemical (PEC) process with simultaneous SO₂ removal and H₂ production has attracted an increasing attention. The proposed process uses flue gas SO₂ to improve H₂ production. The improvement of the efficiency of this process is necessary before it can become industrial viable. Herein, we reported a Mo modified BiVO₄ photocatalysts for a simultaneous SO₂ removal and H₂ production. And the PEC performance could be significantly improved with doping and flue gas removal. The evolution rate of H₂ and removal of SO₂ could be enhanced by almost three times after Mo doping as compared with pristine BiVO₄. The enhanced H₂ production and SO₂ removal is attributed to the improved bulk charge carrier transportation after Mo doping, and greatly enhanced oxidation reaction kinetics on the photoanode due to the formation of SO32− after SO₂ absorption by the electrolyte. Due to the utilization of SO₂ to improve the production of H₂, the proposed PEC process may become a profitable desulfurization technique.

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

Severe haze is a major public health concern in China and India. Both countries rely heavily on coal for energy, and sulfur dioxide (SO2) emitted from coal-fired power plants and industry is a major pollutant contributing to their air quality problems. Timely, accurate information on SO2 sources is a required input to air quality models for pollution prediction and mitigation. However, such information has been difficult to obtain for these two countries, as fast-paced changes in economy and environmental regulations have often led to unforeseen emission changes. Here we use satellite observations to show that China and India are on opposite trajectories for sulfurous pollution. Since 2007, emissions in China have declined by 75% while those in India have increased by 50%. With these changes, India is now surpassing China as the world's largest emitter of anthropogenic SO2. This finding, not predicted by emission scenarios, suggests effective SO2 control in China and lack thereof in India. Despite this, haze remains severe in China, indicating the importance of reducing emissions of other pollutants. In India, ~33 million people now live in areas with substantial SO2 pollution. Continued growth in emissions will adversely affect more people and further exacerbate morbidity and mortality.

Formaldehyde (HCHO) As a Hazardous Air Pollutant: Mapping Surface Air Concentrations from Satellite and Inferring Cancer Risks in the United States

Formaldehyde (HCHO) is the most important carcinogen in outdoor air among the 187 hazardous air pollutants (HAPs) identified by the U.S. Environmental Protection Agency (EPA), not including ozone and particulate matter. However, surface observations of HCHO are sparse and the EPA monitoring network could be prone to positive interferences. Here we use 2005-2016 summertime HCHO column data from the OMI satellite instrument, validated with high-quality aircraft data and oversampled on a 5 × 5 km² grid, to map surface air HCHO concentrations across the contiguous U.S. OMI-derived summertime HCHO values are converted to annual averages using the GEOS-Chem chemical transport model. Results are in good agreement with high-quality summertime observations from urban sites (-2% bias, r = 0.95) but a factor of 1.9 lower than annual means from the EPA network. We thus estimate that up to 6600-12 500 people in the U.S. will develop cancer over their lifetimes by exposure to outdoor HCHO. The main HCHO source in the U.S. is atmospheric oxidation of biogenic isoprene, but the corresponding HCHO yield decreases as the concentration of nitrogen oxides (NO_x ≡ NO + NO₂) decreases. A GEOS-Chem sensitivity simulation indicates that HCHO levels would decrease by 20-30% in the absence of U.S. anthropogenic NO_x emissions. Thus, NO_x emission controls to improve ozone air quality have a significant cobenefit in reducing HCHO-related cancer risks.

Burden of Disease from Rising Coal-Fired Power Plant Emissions in Southeast Asia

Southeast Asia has a very high population density and is on a fast track to economic development, with most of the growth in electricity demand currently projected to be met by coal. From a detailed analysis of coal-fired power plants presently planned or under construction in Southeast Asia, we project in a business-as-usual scenario that emissions from coal in the region will triple to 2.6 Tg a^-1 SO₂ and 2.6 Tg a^-1 NO_x by 2030, with the largest increases occurring in Indonesia and Vietnam. Simulations with the GEOS-Chem chemical transport model show large resulting increases in surface air pollution, up to 11 μg m^-3 for annual mean fine particulate matter (PM_2.5) in northern Vietnam and up to 15 ppb for seasonal maximum 1 h ozone in Indonesia. We estimate 19 880 (11 400-28 400) excess deaths per year from Southeast Asian coal emissions at present, increasing to 69 660 (40 080-126 710) by 2030. 9000 of these excess deaths in 2030 are in China. As Chinese emissions from coal decline in coming decades, transboundary pollution influence from rising coal emissions in Southeast Asia may become an increasing issue.

NOx emission trends over Chinese cities estimated from OMI observations during 2005 to 2015

Satellite NO₂ observations have been widely used to evaluate emission changes. To determine trends in NO_x emission over China, we used a method independent of chemical transport models to quantify the NO_x emissions from 48 cities and 7 power plants over China, on the basis of Ozone Monitoring Instrument (OMI) NO₂ observations during 2005 to 2015. We found that NO_x emissions over 48 Chinese cities increased by 52% from 2005 to 2011 and decreased by 21% from 2011 to 2015. The decrease since 2011 could be mainly attributed to emission control measures in power sector; while cities with different dominant emission sources (i.e. power, industrial and transportation sectors) showed variable emission decline timelines that corresponded to the schedules for emission control in different sectors. The time series of the derived NO_x emissions was consistent with the bottom-up emission inventories for all power plants (r=0.8 on average), but not for some cities (r=0.4 on average). The lack of consistency observed for cities was most probably due to the high uncertainty of bottom-up urban emissions used in this study, which were derived from downscaling the regional-based emission data to cities by using spatial distribution proxies.