Accurate prediction of air quality response to emissions for effective control policy design.

Designing effective control policy requires accurate quantification of the relationship between the ambient concentrations of O3 and PM2.5 and the emissions of their precursors. However, the challenge is that precursor reduction does not necessarily lead to decreases in the concentrations of O3 and PM2.5, which are formed by multiple precursors under complex physical and chemical processes; this calls for the development of advanced model technologies to provide accurate predictions of the nonlinear responses of air quality to emissions. Different from the traditional sensitivity analysis and source apportionment methods, the reduced form models (RFMs) based on chemical transport models (CTMs) are able to quantify air quality responses to emissions more accurately and efficiently with lower computational cost. Here we review recent approaches used in RFMs and compare their structures, advantages and disadvantages, performance and applications. In general, RFMs are classified into three types including (1) sensitivity-based models, (2) models with simplified chemistry and physical processes, and (3) statistical models, with considerable differences in principles, characteristics and application ranges. The prediction of nonlinear responses by RFMs enables more in-depth analysis, not only in terms of real-time prediction of concentrations and quantification of human exposure, health impacts and economic damage, but also in optimizing control policies. Notably, data assimilation and emission inventory inversion based on the nonlinear response of concentrations to emissions can also be greatly beneficial to air pollution control management. In future studies, improvement in the performance of CTMs is exceedingly crucial to obtain a more reliable baseline for the prediction of air quality responses. Development of models to determine the air quality response to emissions under varying meteorological conditions is also necessary in the context of future climate changes, which pose great challenges to the quantification of response relationships. Additionally, with rising requirements for fine-scale air quality management, improving the performance of urban-scale simulations is worth considering. In short, accurate predictions of the response of air quality to emissions, though challenging, holds great promise for the present as well as for future scenarios.

Effect of Future Climate Change on Stratosphere-to-Troposphere-Exchange Driven Ozone in the Northern Hemisphere.

Future estimates of atmospheric pollutant concentrations serve as critical information for policy makers to formulate current policy indicators to achieve future targets. Tropospheric burden of O3 is modulated not only by anthropogenic and natural precursor emissions, but also by the downward transport of O3 associated with stratosphere to troposphere exchange (STE). Hence changes in the estimates of STE and its contributions are key to understand the nature and intensity of future ground level O3 concentrations. The difference in simulated O3 mixing ratios with and without the O3-Potential Vorticity (PV) parameterization scheme is used to represent the model estimated influence of STE on tropospheric O3 distributions. Though STE contributions remain constant in Northern hemisphere as a whole, regional differences exist with Europe (EUR) registering increased STE contribution in both spring and winter while Eastern China (ECH) reporting increased contribution in spring in 2050 (RCP8.5) as compared to 2015. Importance of climate change can be deduced from the fact that ECH and EUR recorded increased STE contribution to O3 in RCP8.5 compared to RCP4.5. Comparison of STE and non-STE meteorological process contributions to O3 due to climate change revealed that contributions of non-STE processes were highest in summer while STE contributions were highest in winter. EUR reported highest STE contribution while ECH reported highest non-STE contribution. None of the 3 regions show consistent low STE contribution due to future climate change (< 50%) in all seasons indicating the significance of STE to ground level O3.

Manganese stabilization in mine tailings by MgO-loaded rice husk biochar: Performance and mechanisms.

Leachable metal in abandoned mine tailings may be toxic to vegetation, affecting effective ecological restoration. In this study, MRB was synthesized through MgCl2·6H2O wet impregnation followed by duplicate slow pyrolysis. Manganese tailings were mixed with MRB, rice husk biochar (RB), and MgO at a dosage of 0-5%, followed by 90-day incubation. Toxicity characteristic leaching procedure and sequential leaching were used to analyze the leachability and species of Mn in tailings, while a stabilization mechanism was proposed with the support of the characterization of the tailings before and after amendment. Results suggested MRB addition significantly decreased leachable Mn by 63.8%, reducing from 59.88 mg/L to 21.68 mg/L, while only a 14.39% reduction was achieved by rice husk biochar (RB). The sharp decline of leachable Mn after 90-day mixing was contributed by the transformation from labile to stable fractions. A microporous biochar matrix along with the uniform dispersion of MgO active component were both responsible for the better Mn stabilization. Only less than 10% of the variation in substrate pH was observed with the increase of MgO loading or incubation time. Linear correlation analyses indicated substrate pH's strongl negative relationship with leachable Mn and moderately positive relationship with residual fraction. Characterization results revealed that MRB exhibited different stabilization mechanisms in mine tailings, where Mn was likely to be stabilized by direct interaction with active MgO or indirect alkaline precipitation to form stable MgMn2O4, Mn(CH3COO)2, and MnO(OH)2. This work validated the promoting potential of recycling agricultural biomass waste for the amendment of manganese mine tailings.

The pathway of impacts of aerosol direct effects on secondary inorganic aerosol formation.

Airborne aerosols reduce surface solar radiation through light scattering and absorption (aerosol direct effects, ADEs), influence regional meteorology, and further affect atmospheric chemical reactions and aerosol concentrations. The inhibition of turbulence and the strengthened atmospheric stability induced by ADEs increases surface primary aerosol concentration, but the pathway of ADE impacts on secondary aerosol is still unclear. In this study, the online coupled meteorological and chemistry model (WRF-CMAQ; Weather Research and Forecasting-Community Multiscale Air Quality) with integrated process analysis was applied to explore how ADEs affect secondary aerosol formation through changes in atmospheric dynamics and photolysis processes. The meteorological condition and air quality in the Jing-Jin-Ji area (denoted JJJ, including Beijing, Tianjin, and Hebei Province in China) in January and July 2013 were simulated to represent winter and summer conditions, respectively. Our results show that ADEs through the photolysis pathway inhibit sulfate formation during winter in the JJJ region and promote sulfate formation in July. The differences are attributed to the alteration of effective actinic flux affected by single-scattering albedo (SSA). ADEs through the dynamics pathway act as an equally or even more important route compared with the photolysis pathway in affecting secondary aerosol concentration in both summer and winter. ADEs through dynamics traps formed sulfate within the planetary boundary layer (PBL) which increases sulfate concentration in winter. Meanwhile, the impact of ADEs through dynamics is mainly reflected in the increase of gaseous-precursor concentrations within the PBL which enhances secondary aerosol formation in summer. For nitrate, reduced upward transport of precursors restrains the formation at high altitude and eventually lowers the nitrate concentration within the PBL in winter, while such weakened vertical transport of precursors increases nitrate concentration within the PBL in summer, since nitrate is mainly formed near the surface ground.

Rapid Inference of Nitrogen Oxide Emissions Based on a Top-Down Method with a Physically Informed Variational Autoencoder.

Accurate timely estimation of emissions of nitrogen oxides (NOx) is a prerequisite for designing an effective strategy for reducing O3 and PM2.5 pollution. The satellite-based top-down method can provide near-real-time constraints on emissions; however, its efficiency is largely limited by efforts in dealing with the complex emission-concentration response. Here, we propose a novel machine-learning-based method using a physically informed variational autoencoder (VAE) emission predictor to infer NOx emissions from satellite-retrieved surface NO2 concentrations. The computational burden can be significantly reduced with the help of a neural network trained with a chemical transport model, allowing the VAE emission predictor to provide a timely estimation of posterior emissions based on the satellite-retrieved surface NO2 concentration. The VAE emission predictor successfully corrected the underestimation of NOx emissions in rural areas and the overestimation in urban areas, resulting in smaller normalized mean biases (reduced from -0.8 to -0.4) and larger R2 values (increased from 0.4 to 0.7). The interpretability of the VAE emission predictor was investigated using sensitivity analysis by modulating each feature, indicating that NO2 concentration and planetary boundary layer (PBL) height are important for estimating NOx emissions, which is consistent with our common knowledge. The advantages of the VAE emission predictor in efficiency, flexibility, and accuracy demonstrate its great potential in estimating the latest emissions and evaluating the control effectiveness from observations.

Ozone pollution in China: Background and transboundary contributions to ozone concentration & related health effects across the country.

China experiences high ozone concentrations with highest annual 8-hour maximum concentration in eastern China of 78 µg/m3 and was followed by southern (73 µg/m3), north-western (69 µg/m3), northern (68 µg/m3), central (67 µg/m3), north eastern (65 µg/m3) and south-western China (59 µg/m3). Ozone concentration peaked in spring season in 4 (eastern, northern, north eastern & central) of 7 regions across China while lowest concentration in most regions across China was experienced in winter season with central and southern China being the only exceptions. Regions outside Asia contributed ozone to all regions across China with highest contributions in 4 (East, Central, North & Northeast) of the 7 regions. South-western China had the largest ozone contribution from outside (23%) and was followed by 16.39% outside ozone contribution in north-western China, 11.64% contribution in north eastern China, 11% contribution in northern China, 7.85% contribution in southern China, 5.28% contribution in central China while 4.56% contribution in eastern China. Policy relevant background (PRB) concentration was above 47 µg/m3 in all regions across China and contributed about 71-94% to total ozone concentration with highest PRB concentration of 65.25 µg/m3 recorded in north-west China. China recorded 93,351 (95%CI: 11001-169,898) ozone related premature mortality in 2015 with 42,673 (95%CI: 11001-69,586) respiratory mortality and 50,678 (95%CI: 0-100,312) cardiovascular mortality. Northern and eastern China recorded high ozone related mortality with 18,230 (95%CI: 4700-29,727) & 12,261 (95%CI: 3161-19,993) respiratory and 21,662 (95%CI: 0-42,877) & 14,528 (95%CI: 0-28,757) cardiovascular deaths respectively. In terms of foreign contributions, premature mortality due to ozone from outside Asia contributed the most to China with 1070 (95%CI: 276-1746) respiratory mortality and 1270 (95%CI: 0-2515) cardiovascular mortality. East Asia contributed to about 419 (95%CI: 109-679) respiratory deaths and 501 (95%CI: 0-989) cardiovascular deaths while North Asia contributed to 220 (95%CI: 56-358) respiratory mortality and 260 (95%CI, 0-515) cardiovascular mortality.

Estimating ground level PM2.5 concentrations and associated health risk in India using satellite based AOD and WRF predicted meteorological parameters.

PM2.5 concentrations in most of the Indian cities are in alarming levels. However, the current network of 308 monitoring stations are heterogeneously placed and do not cover many parts of the country. This limits the ability of agencies to measure the concentration which people are exposed to. In this study, ground level PM2.5 concentrations and the associated risk and mortality in India using satellite based AOD data for the year 2015 was estimated to identify the state specific number of more monitoring sites required. Results indicate that average PM2.5 concentrations were 89 µg/m3, which caused 1.61 million deaths including 0.34 million Chronic Obstructive Pulmonary Disease (COPD) deaths, 0.2 million Lung Cancer (LC) deaths, 0.53 million Ischemic Heart Disease (IHD) deaths and 0.70 million deaths due to Stroke. The years of life lost (YLL) per 1000 population due to exposure to PM2.5 indicated Delhi (North-India) to be severely affected by PM2.5 resulting in 227.47 years of life lost and was closely followed by Bihar (Eastern-India) (225.18), Rajasthan (Western-India) (225.05) and Uttar Pradesh (Northern-India) (213.16). Eastern India had the highest population weighted concentration (102.09 µg/m3) and contributed to 23.46% of premature mortality and was followed by Central (75.32 µg/m3) and Northern India (75.12 µg/m3), thus indicating severity of air pollution in India and need for its constant monitoring. As per Indian regulatory agency's guidelines, India still needs 1638 more air quality monitoring stations, of which North-Indian states require maximum number of additional stations i.e. 400, followed by 382 in eastern states.

The impact of aerosol direct radiative effects on PM2.5-related health risk in Northern Hemisphere during 2013-2017.

This study estimates the health risk due to PM2.5 and ADE primarily in 4 regions across Northern Hemisphere during 2013-2017. Mortality in China due to PM2.5 dipped from 1.15 million (95%CI (Confidence Interval) 0.64-1.6 million) to 1.02 million (95%CI 0.59-1.49 million) during 2013-2017 as a result of reduction in PM2.5 population weighted concentration (PWC) from 29.26 µg/m3 to 22.05 µg/m3 while India overtook China in terms of death due to PM2.5 which increased to 1.16 million (95%CI 0.72-1.67 million) from 1.07 million (95%CI 0.62-1.53 million) as a result of increase in PWC from 38.18 to 44.47 µg/m3. The years of life lost per person (YLL/person) due to PM2.5 was still observed to be high in China with 5.58 YLL/person followed by India (4.13), Europe (2.19) and US (0.46) in 2017. Aerosols such as PM2.5 have the capability to scatter or absorb solar radiation resulting in perturbation of ground meteorology which further affects dispersion of pollutants and it's resultant health impacts. ADE resulted in 7.27% of total or 77,866 deaths in India during 2013 which increased to 8.05% or 93,575 deaths in 2017 which was highest among all regions while in China ADE deaths reduced from 59,529 (5.15% of total) to 40,470 (3.94% of total) deaths during the same period, other regions too reported reduction in ADE deaths with US reporting 906 (-1.27%) lower deaths while Europe also reported 785 (-0.46%) lower deaths in 2017 as compared to 2013. ADE resulted in increased YLL/person in India from 0.29 to 0.33 during 2013-2017 while it was observed to reduce in all other regions, in China it reduced from 0.37 to 0.22 likewise YLL/person also reduced in US from 0.04 to 0.01 and in Europe from 0.01 to 0.002.

Characterization and health risks of criteria air pollutants in Delhi, 2017.

Severe air pollution events were observed frequently in north India in recent years especially at its capital, Delhi. Criteria air pollutants data at 10 sites for 2017 in Delhi were analyzed. The results show annual fine particulate matter (PM2.5) concentrations exceeded the National Ambient Air Quality Standards (NAAQS) of 60 µg/m3 at all sites from 105.51 (site 10) to 143.23 µg/m3 (site 7). Sub-urban sites (site 8, 9 and 10) had lower PM2.5 concentrations than urban sites. Coarse PM (PM10) and ozone (O3) were also important with annual averages of 399.56 µg/m3 and 75.69 ppb, respectively. Peak PM2.5 occurred at the Diwali in early November and Christmas. Only PM10 showed a significant weekly difference with a weekdays/weekends ratio of ∼1.5. PM2.5/PM10 ratio in episode days with PM2.5 of >60 µg/m3 was higher than non-episode days. Pearson correlation coefficients show O3 was negatively related with CO, SO2, and NO2, while PM2.5 was positively related to these pollutants. Analysis of two extreme events from Nov. 6th to Nov. 14th and Dec. 18th to Dec. 26th shows that meteorological conditions with low wind speed and warm temperature kept PM2.5 concentrations at a high level during these events. Backward trajectory and cluster analysis show the wind coming from northwest of Delhi, where massive anthropogenic emissions were generated, led to high concentrations of air pollutants to Delhi. Health risk analysis reveals that PM2.5 and PM10 were the two major pollutants threatening public health among the six criteria pollutants.

Source apportionment of PM2.5 in North India using source-oriented air quality models.

In recent years, severe pollution events were observed frequently in India especially at its capital, New Delhi. However, limited studies have been conducted to understand the sources to high pollutant concentrations for designing effective control strategies. In this work, source-oriented versions of the Community Multi-scale Air Quality (CMAQ) model with Emissions Database for Global Atmospheric Research (EDGAR) were applied to quantify the contributions of eight source types (energy, industry, residential, on-road, off-road, agriculture, open burning and dust) to fine particulate matter (PM2.5) and its components including primary PM (PPM) and secondary inorganic aerosol (SIA) i.e. sulfate, nitrate and ammonium ions, in Delhi and three surrounding cities, Chandigarh, Lucknow and Jaipur in 2015. PPM mass is dominated by industry and residential activities (>60%). Energy (∼39%) and industry (∼45%) sectors contribute significantly to PPM at south of Delhi, which reach a maximum of 200 µg/m3 during winter. Unlike PPM, SIA concentrations from different sources are more heterogeneous. High SIA concentrations (∼25 µg/m3) at south Delhi and central Uttar Pradesh were mainly attributed to energy, industry and residential sectors. Agriculture is more important for SIA than PPM and contributions of on-road and open burning to SIA are also higher than to PPM. Residential sector contributes highest to total PM2.5 (∼80 µg/m3), followed by industry (∼70 µg/m3) in North India. Energy and agriculture contribute ∼25 µg/m3 and ∼16 µg/m3 to total PM2.5, while SOA contributes <5 µg/m3. In Delhi, industry and residential activities contribute to 80% of total PM2.5.