Showing papers on "Air quality index published in 2019"

Drivers of improved PM2.5 air quality in China from 2013 to 2017.

Abstract: From 2013 to 2017, with the implementation of the toughest-ever clean air policy in China, significant declines in fine particle (PM2.5) concentrations occurred nationwide. Here we estimate the drivers of the improved PM2.5 air quality and the associated health benefits in China from 2013 to 2017 based on a measure-specific integrated evaluation approach, which combines a bottom-up emission inventory, a chemical transport model, and epidemiological exposure-response functions. The estimated national population-weighted annual mean PM2.5 concentrations decreased from 61.8 (95%CI: 53.3-70.0) to 42.0 µg/m3 (95% CI: 35.7-48.6) in 5 y, with dominant contributions from anthropogenic emission abatements. Although interannual meteorological variations could significantly alter PM2.5 concentrations, the corresponding effects on the 5-y trends were relatively small. The measure-by-measure evaluation indicated that strengthening industrial emission standards (power plants and emission-intensive industrial sectors), upgrades on industrial boilers, phasing out outdated industrial capacities, and promoting clean fuels in the residential sector were major effective measures in reducing PM2.5 pollution and health burdens. These measures were estimated to contribute to 6.6- (95% CI: 5.9-7.1), 4.4- (95% CI: 3.8-4.9), 2.8- (95% CI: 2.5-3.0), and 2.2- (95% CI: 2.0-2.5) µg/m3 declines in the national PM2.5 concentration in 2017, respectively, and further reduced PM2.5-attributable excess deaths by 0.37 million (95% CI: 0.35-0.39), or 92% of the total avoided deaths. Our study confirms the effectiveness of China's recent clean air actions, and the measure-by-measure evaluation provides insights into future clean air policy making in China and in other developing and polluting countries.

A two-pollutant strategy for improving ozone and particulate air quality in China

Abstract: Fine particulate matter (PM2.5) decreased by 30–40% across China during 2013–2017 in response to the governmental Clean Air Action. However, surface ozone pollution worsened over the same period. Model simulations have suggested that the increase in ozone could be driven by the decrease in PM2.5, because PM2.5 scavenges hydroperoxy (HO2) and NOx radicals that would otherwise produce ozone. Here we show observational evidence for this effect with 2013–2018 summer data of hourly ozone and PM2.5 concentrations from 106 sites in the North China Plain. The observations show suppression of ozone pollution at high PM2.5 concentrations, consistent with a model simulation in which PM2.5 scavenging of HO2 and NOx depresses ozone concentrations by 25 ppb relative to PM2.5-free conditions. PM2.5 chemistry makes ozone pollution less sensitive to NOx emission controls, emphasizing the need for controlling emissions of volatile organic compounds (VOCs), which so far have not decreased in China. The new 2018–2020 Clean Air Action plan calls for a 10% decrease in VOC emissions that should begin to reverse the long-term ozone increase even as PM2.5 continues to decrease. Aggressive reduction of NOx and aromatic VOC emissions should be particularly effective for decreasing both PM2.5 and ozone. Observations confirm that cleaning up fine particulate matter in the North China Plain has exacerbated ozone pollution, suggesting that both NOx and VOC emissions need to be reduced to improve air quality.

Air pollution lowers Chinese urbanites’ expressed happiness on social media

Abstract: High levels of air pollution in China may contribute to the urban population’s reported low level of happiness1–3. To test this claim, we have constructed a daily city-level expressed happiness metric based on the sentiment in the contents of 210 million geotagged tweets on the Chinese largest microblog platform Sina Weibo4–6, and studied its dynamics relative to daily local air quality index and PM2.5 concentrations (fine particulate matter with diameters equal or smaller than 2.5 μm, the most prominent air pollutant in Chinese cities). Using daily data for 144 Chinese cities in 2014, we document that, on average, a one standard deviation increase in the PM2.5 concentration (or Air Quality Index) is associated with a 0.043 (or 0.046) standard deviation decrease in the happiness index. People suffer more on weekends, holidays and days with extreme weather conditions. The expressed happiness of women and the residents of both the cleanest and dirtiest cities are more sensitive to air pollution. Social media data provides real-time feedback for China’s government about rising quality of life concerns. A daily, city-level happiness metric constructed from the sentiment expressed in 210 million tweets on Sina Weibo from 144 cities shows that high levels of air pollution significantly reduce Chinese urbanites’ expressed happiness on social media.

Dominant role of emission reduction in PM 2.5 air quality improvement in Beijing during 2013–2017: a model-based decomposition analysis

Abstract: . In 2013, China's government published the Air Pollution Prevention and Control Action Plan (APPCAP) with a specific target for Beijing, which aims to reduce annual mean PM 2.5 concentrations in Beijing to 60 µ g m −3 in 2017. During 2013–2017, the air quality in Beijing was significantly improved following the implementation of various emission control measures locally and regionally, with the annual mean PM 2.5 concentration decreasing from 89.5 µ g m −3 in 2013 to 58 µ g m −3 in 2017. As meteorological conditions were more favourable to the reduction of air pollution in 2017 than in 2013 and 2016, the real effectiveness of emission control measures on the improvement of air quality in Beijing has frequently been questioned. In this work, by combining a detailed bottom-up emission inventory over Beijing, the MEIC regional emission inventory and the WRF-CMAQ (Weather Research and Forecasting Model and Community Multiscale Air Quality) model, we attribute the improvement in Beijing's PM 2.5 air quality in 2017 (compared to 2013 and 2016) to the following factors: changes in meteorological conditions, reduction of emissions from surrounding regions, and seven specific categories of local emission control measures in Beijing. We collect and summarize data related to 32 detailed control measures implemented during 2013–2017, quantify the emission reductions associated with each measure using the bottom-up local emission inventory in 2013, aggregate the measures into seven categories, and conduct a series of CMAQ simulations to quantify the contribution of different factors to the PM 2.5 changes. We found that, although changes in meteorological conditions partly explain the improved PM 2.5 air quality in Beijing in 2017 compared to 2013 (3.8 µ g m −3 , 12.1 % of total), the rapid decrease in PM 2.5 concentrations in Beijing during 2013–2017 was dominated by local (20.6 µ g m −3 , 65.4 %) and regional (7.1 µ g m −3 , 22.5 %) emission reductions. The seven categories of emission control measures, i.e. coal-fired boiler control, clean fuels in the residential sector, optimize industrial structure, fugitive dust control, vehicle emission control, improved end-of-pipe control, and integrated treatment of VOCs, reduced the PM 2.5 concentrations in Beijing by 5.9, 5.3, 3.2, 2.3, 1.9, 1.8, and 0.2 µ g m −3 , respectively, during 2013–2017. We also found that changes in meteorological conditions could explain roughly 30 % of total reduction in PM 2.5 concentration during 2016–2017 with more prominent contribution in winter months (November and December). If the meteorological conditions in 2017 had remained the same as those in 2016, the annual mean PM 2.5 concentrations would have increased from 58 to 63 µ g m −3 , exceeding the target established in the APPCAP. Despite the remarkable impacts from meteorological condition changes, local and regional emission reductions still played major roles in the PM 2.5 decrease in Beijing during 2016–2017, and clean fuels in the residential sector, coal-fired boiler control, and optimize industrial structure were the three most effective local measures (contributing reductions of 2.1, 1.9, and 1.5 µ g m −3 , respectively). Our study confirms the effectiveness of clean air actions in Beijing and its surrounding regions and reveals that a new generation of control measures and strengthened regional joint emission control measures should be implemented for continued air quality improvement in Beijing because the major emitting sources have changed since the implementation of the clean air actions.

Internet of Things Mobile–Air Pollution Monitoring System (IoT-Mobair)

Abstract: Internet of Things (IoT) is a worldwide system of “smart devices” that can sense and connect with their surroundings and interact with users and other systems. Global air pollution is one of the major concerns of our era. Existing monitoring systems have inferior precision, low sensitivity, and require laboratory analysis. Therefore, improved monitoring systems are needed. To overcome the problems of existing systems, we propose a three-phase air pollution monitoring system. An IoT kit was prepared using gas sensors, Arduino integrated development environment (IDE), and a Wi-Fi module. This kit can be physically placed in various cities to monitoring air pollution. The gas sensors gather data from air and forward the data to the Arduino IDE. The Arduino IDE transmits the data to the cloud via the Wi-Fi module. We also developed an Android application termed IoT-Mobair , so that users can access relevant air quality data from the cloud. If a user is traveling to a destination, the pollution level of the entire route is predicted, and a warning is displayed if the pollution level is too high. The proposed system is analogous to Google traffic or the navigation application of Google Maps. Furthermore, air quality data can be used to predict future air quality index (AQI) levels.

Impacts of climate change on future air quality and human health in China

Abstract: In recent years, air pollution has caused more than 1 million deaths per year in China, making it a major focus of public health efforts. However, future climate change may exacerbate such human health impacts by increasing the frequency and duration of weather conditions that enhance air pollution exposure. Here, we use a combination of climate, air quality, and epidemiological models to assess future air pollution deaths in a changing climate under Representative Concentration Pathway 4.5 (RCP4.5). We find that, assuming pollution emissions and population are held constant at current levels, climate change would adversely affect future air quality for >85% of China's population (∼55% of land area) by the middle of the century, and would increase by 3% and 4% the population-weighted average concentrations of fine particulate matter (PM2.5) and ozone, respectively. As a result, we estimate an additional 12,100 and 8,900 Chinese (95% confidence interval: 10,300 to 13,800 and 2,300 to 14,700, respectively) will die per year from PM2.5 and ozone exposure, respectively. The important underlying climate mechanisms are changes in extreme conditions such as atmospheric stagnation and heat waves (contributing 39% and 6%, respectively, to the increase in mortality). Additionally, greater vulnerability of China's aging population will further increase the estimated deaths from PM2.5 and ozone in 2050 by factors of 1 and 3, respectively. Our results indicate that climate change and more intense extremes are likely to increase the risk of severe pollution events in China. Managing air quality in China in a changing climate will thus become more challenging.

Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique

Abstract: . A 5-year Clean Air Action Plan was implemented in 2013 to reduce air pollutant emissions and improve ambient air quality in Beijing. Assessment of this action plan is an essential part of the decision-making process to review its efficacy and to develop new policies. Both statistical and chemical transport modelling have been previously applied to assess the efficacy of this action plan. However, inherent uncertainties in these methods mean that new and independent methods are required to support the assessment process. Here, we applied a machine-learning-based random forest technique to quantify the effectiveness of Beijing's action plan by decoupling the impact of meteorology on ambient air quality. Our results demonstrate that meteorological conditions have an important impact on the year-to-year variations in ambient air quality. Further analyses show that the PM 2.5 mass concentration would have broken the target of the plan (2017 annual PM 2.5 µ g m −3 ) were it not for the meteorological conditions in winter 2017 favouring the dispersion of air pollutants. However, over the whole period (2013–2017), the primary emission controls required by the action plan have led to significant reductions in PM 2.5 , PM 10 , NO2 , SO2 , and CO from 2013 to 2017 of approximately 34 %, 24 %, 17 %, 68 %, and 33 %, respectively, after meteorological correction. The marked decrease in PM 2.5 and SO2 is largely attributable to a reduction in coal combustion. Our results indicate that the action plan has been highly effective in reducing the primary pollution emissions and improving air quality in Beijing. The action plan offers a successful example for developing air quality policies in other regions of China and other developing countries.

Rapid transition in winter aerosol composition in Beijing from 2014 to 2017: response to clean air actions

Abstract: . The clean air actions implemented by the Chinese government in 2013 have led to significantly improved air quality in Beijing. In this work, we combined the in situ measurements of the chemical components of submicron particles (PM 1 ) in Beijing during the winters of 2014 and 2017 and a regional chemical transport model to investigate the impact of clean air actions on aerosol chemistry and quantify the relative contributions of anthropogenic emissions, meteorological conditions, and regional transport to the changes in aerosol chemical composition from 2014 to 2017. We found that the average PM 1 concentration in winter in Beijing decreased by 49.5 % from 2014 to 2017 (from 66.2 to 33.4 µ g m −3 ). Sulfate exhibited a much larger decline than nitrate and ammonium, which led to a rapid transition from sulfate-driven to nitrate-driven aerosol pollution during the wintertime. Organic aerosol (OA), especially coal combustion OA, and black carbon also showed large decreasing rates, indicating the effective emission control of coal combustion and biomass burning. The decreased sulfate contribution and increased nitrate fraction were highly consistent with the much faster emission reductions in sulfur dioxide ( SO2 ) due to phasing out coal in Beijing compared to reduction in nitrogen oxides emissions estimated by bottom-up inventory. The chemical transport model simulations with these emission estimates reproduced the relative changes in aerosol composition and suggested that the reduced emissions in Beijing and its surrounding regions played a dominant role. The variations in meteorological conditions and regional transport contributed much less to the changes in aerosol concentration and its chemical composition during 2014–2017 compared to the decreasing emissions. Finally, we speculated that changes in precursor emissions possibly altered the aerosol formation mechanisms based on ambient observations. The observed explosive growth of sulfate at a relative humidity (RH) greater than 50 % in 2014 was delayed to a higher RH of 70 % in 2017, which was likely caused by the suppressed sulfate formation through heterogeneous reactions due to the decrease in SO2 emissions. Thermodynamic simulations showed that the decreased sulfate and nitrate concentrations have lowered the aerosol water content, particle acidity, and ammonium particle fraction. The results in this study demonstrate the response of aerosol chemistry to the stringent clean air actions and identify that the anthropogenic emission reductions are a major driver, which could help to further guide air pollution control strategies in China.

Textiles/Metal-Organic Frameworks Composites as Flexible Air Filters for Efficient Particulate Matter Removal.

Abstract: The health-threatening air pollution, especially from particulate matter (PM), has triggered increasing demands for developing low-cost and long-service-life air-cleaning technologies. In the present contribution, a range of high-efficiency textiles/metal-organic framework (MOF) composites (MOFs@textiles) air filters with excellent washable reusability is presented. By processing MOFs onto textile substrates via an eco-friendly solvent-free method to enable the microporous feature and also strong PM adhesion, we develop flexible, highly effective air filters with >95.00% PM removal efficiency (e.g., MiL-53(Al)@Aramid, PM2.5: 95.30%, PM10: 96.11%) under harmful air quality conditions (average PM2.5 mass concentration > 280 μg m-3 and PM10 > 360 μg m-3). Therefore, these MOFs@textiles are promising composites for producing efficient and recyclable out-/indoor air purifiers.

Indian annual ambient air quality standard is achievable by completely mitigating emissions from household sources.

Abstract: Exposures to ambient and household fine-particulate matter (PM2.5) together are among the largest single causes of premature mortality in India according to the Global Burden of Disease Studies (GBD). Several recent investigations have estimated that household emissions are the largest contributor to ambient PM2.5 exposure in the country. Using satellite-derived district-level PM2.5 exposure and an Eulerian photochemical dispersion model CAMx (Comprehensive Air Quality Model with Extensions), we estimate the benefit in terms of population exposure of mitigating household sources--biomass for cooking, space- and water-heating, and kerosene for lighting. Complete mitigation of emissions from only these household sources would reduce India-wide, population-weighted average annual ambient PM2.5 exposure by 17.5, 11.9, and 1.3%, respectively. Using GBD methods, this translates into reductions in Indian premature mortality of 6.6, 5.5, and 0.6%. If PM2.5 emissions from all household sources are completely mitigated, 103 (of 597) additional districts (187 million people) would meet the Indian annual air-quality standard (40 μg m-3) compared with baseline (2015) when 246 districts (398 million people) met the standard. At 38 μg m-3, after complete mitigation of household sources, compared with 55.1 μg m-3 at baseline, the mean annual national population-based concentration would meet the standard, although highly polluted areas, such as Delhi, would remain out of attainment. Our results support expansion of programs designed to promote clean household fuels and rural electrification to achieve improved air quality at regional scales, which also has substantial additional health benefits from directly reducing household air pollution exposures.

Air pollution knowledge assessments (APnA) for 20 Indian cities

Abstract: Delhi, with a population of 22 million (1.6% of national total) is one of the most polluted capital cities in the world. Nearly 50% of the published literature in India focus on air pollution in Delhi. However, air pollution impacts are not limited only to the capital city. Yet, there is little information and attempt to quantify these impacts for Tier 1 and 2 cities, even though they account for >30% of India's population. To remedy this vacuum of information, the Air Pollution knowledge Assessments (APnA) city program deliberately focuses on 20 Indian cities, other than Delhi. We established baseline multi-pollutant high-resolution emissions inventory, after collating information from multiple resources detailed in this paper, which was used to estimate spatial concentrations of key pollutants across city's urban airshed using WRF-CAMx chemical transport modeling system. The inventory includes anthropogenic sources, such as transport (road, rail, ship, and aviation), large scale power generation (from coal, diesel, and gas power plants), small scale power generation (from diesel generator sets for household use, commercial use, and agricultural water pumping), small and medium scale industries, dust (road resuspension and construction), domestic (cooking, heating, and lighting), open waste burning, and open fires and non-anthropogenic sources, such as sea salt, dust storms, biogenic, and lightning. The emissions inventory is currently in use for 3-day advance air quality forecasting for public release on an on-going basis. Using meteorological parameters and big data like gridded speed maps from google, the emissions inventory is dynamically updated. The results from this research will be valuable to local and national policy makers - especially the information on source contributions to air pollution.

Performance assessment of a low-cost PM2.5 Sensor for a near four-month period in Oslo, Norway

Abstract: The very low-cost Nova particulate matter (PM) sensor SDS011 has recently drawn attention for its use for measuring PM mass concentration, which is frequently used as an indicator of air quality. However, this sensor has not been thoroughly evaluated in real-world conditions and its data quality is not well documented. In this study, three SDS011 sensors were evaluated by co-locating them at an official, air quality monitoring station equipped with reference-equivalent instrumentation in Oslo, Norway. The sensors’ measurement results for PM2.5 were compared with data generated from the air quality monitoring station over almost a four-month period. Five performance aspects of the sensors were examined: operational data coverage, linearity of response and accuracy, inter-sensor variability, dependence on relative humidity (RH) and temperature (T), and potential improvement of sensor accuracy, by data calibration using a machine-learning method. The results of the study are: (i) the three sensors provide quite similar results, with inter-sensor correlations exhibiting R values higher than 0.97; (ii) all three sensors demonstrate quite high linearity against officially measured concentrations of PM2.5, with R2 values ranging from 0.55 to 0.71; (iii) high RH (over 80%) negatively affected the sensor response; (iv) data calibration using only the RH and T recorded directly at the three sensors increased the R2 value from 0.71 to 0.80, 068 to 0.79, and 0.55 to 0.76. The results demonstrate the general feasibility of using these low cost SDS011 sensors for indicative PM2.5 monitoring under certain environmental conditions. Within these constraints, they further indicate that there is potential for deploying large networks of such devices, due to the sensors’ relative accuracy, size and cost. This opens up a wide variety of applications, such as high-resolution air quality mapping and personalized air quality information services. However, it should be noted that the sensors exhibit often very high relative errors for hourly values and that there is a high potential of abusing these types of sensors if they are applied outside the manufacturer-provided specifications particularly regarding relative humidity. Furthermore, our analysis covers only a relatively short time period and it is desirable to carry out longer-term studies covering a wider range of meteorological conditions.

Effects of air pollution control policies on PM 2.5 pollution improvement in China from 2005 to 2017: a satellite-based perspective

Abstract: . Understanding the effectiveness of air pollution control policies is important for future policy making. China has implemented strict air pollution control policies since the 11th Five-Year Plan (FYP). There is still a lack of overall evaluation of the effects of air pollution control policies on PM2.5 pollution improvement in China since the 11th FYP. In this study, we aimed to assess the effects of air pollution control policies from 2005 to 2017 on PM2.5 using satellite remote sensing. We used the satellite-derived PM2.5 of 2005–2013 from one of our previous studies. For the data of 2014–2017, we developed a two-stage statistical model to retrieve satellite PM2.5 data using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD), assimilated meteorology, and land use data. The first stage is a day-specific linear mixed effects (LME) model and the second stage is a generalized additive model (GAM). Results show that the Energy Conservation and Emissions Reduction (ECER) policy, implemented in the 11th FYP period and focused on SO2 emissions control, had co-benefits with PM2.5 reductions. The increasing trends of PM2.5 pollution (1.88 and 3.14 µ g m −3 year −1 for all of China and the Jingjinji region in 2004–2007, p ) were suppressed after 2007. The overall PM2.5 trend for all of China was −0.56 µ g m −3 year −1 with marginal significance ( p=0.053 ) and PM2.5 concentrations in the Pearl River Delta region had a big drop ( −4.81 µ g m −3 year −1 , p ) in 2007–2010. The ECER policy during the 12th FYP period was basically an extension of the 11th FYP policy. PM2.5 is a kind of composite pollutant which comprises primary particles and secondary particles such as sulfate, nitrate, ammonium, organic carbon, elemental carbon, etc. Since the ECER policy focused on single-pollutant control, it had shown great limitation for PM2.5 reductions. The PM2.5 concentrations did not decrease from 2010 to 2013 in polluted areas ( p values of the trends were greater than 0.05). Therefore, China implemented two stricter policies: the 12th FYP on Air Pollution Prevention and Control in Key Regions (APPC-KR) in 2012, and the Action Plan of Air Pollution Prevention and Control (APPC-AP) in 2013. The goal of air quality improvement (especially PM2.5 concentration improvement) and measures for multi-pollutant control were proposed. These policies led to dramatic decreases in PM2.5 after 2013 ( −4.27 µ g m −3 year −1 for all of China in 2013–2017, p ).

Significant reduction of PM 2.5 in eastern China due to regional-scale emission control: evidence from SORPES in 2011–2018

Abstract: . Haze pollution caused by PM 2.5 is the largest air quality concern in China in recent years. Long-term measurements of PM 2.5 and the precursors and chemical speciation are crucially important for evaluating the efficiency of emission control, understanding formation and transport of PM 2.5 associated with the change of meteorology, and accessing the impact of human activities on regional climate change. Here we reported long-term continuous measurements of PM 2.5 , chemical components, and their precursors at a regional background station, the Station for Observing Regional Processes of the Earth System (SORPES), in Nanjing, eastern China, since 2011. We found that PM 2.5 at the station has experienced a substantial decrease ( −9.1 % yr −1 ), accompanied by even a very significant reduction of SO2 ( −16.7 % yr −1 ), since the national “Ten Measures of Air” took action in 2013. Control of open biomass burning and fossil-fuel combustion are the two dominant factors that influence the PM 2.5 reduction in early summer and winter, respectively. In the cold season (November–January), the nitrate fraction was significantly increased, especially when air masses were transported from the north. More NH3 available from a substantial reduction of SO2 and increased oxidization capacity are the main factors for the enhanced nitrate formation. The changes of year-to-year meteorology have contributed to 24 % of the PM 2.5 decrease since 2013. This study highlights several important implications on air pollution control policy in China.

Air quality and health benefits from fleet electrification in China

Abstract: China has emerged as a leading electric vehicle (EV) market, accounting for approximately half of the global EV sales volume. We employed an atmospheric chemistry model to evaluate the air quality impacts from multiple scenarios by considering various EV penetration levels in China and assessed the avoided premature mortality attributed to fine particulate matter and ozone pollution. We find higher fleet electrification ratios can synergistically deliver greater air quality, climate and health benefits. For example, electrifying 27% of private vehicles and a larger proportion of certain commercial fleets can readily reduce the annual concentrations of fine particulate matter, nitrogen dioxide and summer concentrations of ozone by 2030. This scenario can reduce the number of annual premature deaths nationwide by 17,456 (95% confidence interval: 10,656–22,160), with the Beijing–Tianjin–Hebei, Yangtze River Delta and Pearl River Delta regions accounting for ~37% of the total number. The high concentration of health benefits in populous megacities implies that their municipal governments should promote more supportive local incentives. This study further reveals that fleet electrification in China could have more health benefits than net climate benefits in the next decade, which should be realized by policymakers to develop cost-effective strategies for EV development. Wide adoption of electric vehicles can contribute to mitigate climate change and air pollution. Here the authors develop various fleet electrification scenarios in China to evaluate the associated air quality and health impacts to inform sound policies.

Impacts of air pollutants from rural Chinese households under the rapid residential energy transition

Abstract: Rural residential energy consumption in China is experiencing a rapid transition towards clean energy, nevertheless, solid fuel combustion remains an important emission source. Here we quantitatively evaluate the contribution of rural residential emissions to PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm) and the impacts on health and climate. The clean energy transitions result in remarkable reductions in the contributions to ambient PM2.5, avoiding 130,000 (90,000-160,000) premature deaths associated with PM2.5 exposure. The climate forcing associated with this sector declines from 0.057 ± 0.016 W/m2 in 1992 to 0.031 ± 0.008 W/m2 in 2012. Despite this, the large remaining quantities of solid fuels still contributed 14 ± 10 μg/m3 to population-weighted PM2.5 in 2012, which comprises 21 ± 14% of the overall population-weighted PM2.5 from all sources. Rural residential emissions affect not only rural but urban air quality, and the impacts are highly seasonal and location dependent.

Air quality in megacity Delhi affected by countryside biomass burning

Abstract: South Asian megacities are strong sources of regional air pollution. Delhi is a key hotspot of health-and climate-impacting black carbon (BC) emissions, affecting environmental sustainability in de ...

Connecting Crop Productivity, Residue Fires, and Air Quality over Northern India.

Abstract: Northwestern India is known as the “breadbasket” of the country producing two-thirds of food grains, with wheat and rice as the principal crops grown under the crop rotation system. Agricultural data from India indicates a 25% increase in the post-monsoon rice crop production in Punjab during 2002–2016. NASA’s A-train satellite sensors detect a consistent increase in the vegetation index (net 21%) and post-harvest agricultural fire activity (net ~60%) leading to nearly 43% increase in aerosol loading over the populous Indo-Gangetic Plain in northern India. The ground-level particulate matter (PM2.5) downwind over New Delhi shows a concurrent uptrend of net 60%. The effectiveness of a robust satellite-based relationship between vegetation index—a proxy for crop amounts, and post-harvest fires—a precursor of extreme air pollution events, has been further demonstrated in predicting the seasonal agricultural burning. An efficient crop residue management system is critically needed towards eliminating open field burning to mitigate episodic hazardous air quality over northern India.