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Growing evidence suggests that fine particulate matter (PM 2.5 ) likely increases the risks of dementia, yet little is known about the relative contributions of different constituents. Here, we conducted a nationwide population-based cohort study (2000 to 2017) by integrating the Medicare Chronic Conditions Warehouse database and two independently sourced datasets of high-resolution PM 2.5 major chemical composition, including black carbon (BC), organic matter (OM), nitrate (NO 3 - ), sulfate (SO 4 2- ), ammonium (NH 4 + ), and soil dust (DUST). To investigate the impact of long-term exposure to PM 2.5 constituents on incident all-cause dementia and Alzheimer's disease (AD), hazard ratios for dementia and AD were estimated using Cox proportional hazards models, and penalized splines were used to evaluate potential nonlinear concentration-response (C-R) relationships. Results using two exposure datasets consistently indicated higher rates of incident dementia and AD for an increased exposure to PM 2.5 and its major constituents. An interquartile range increase in PM 2.5 mass was associated with a 6 to 7% increase in dementia incidence and a 9% increase in AD incidence. For different PM 2.5 constituents, associations remained significant for BC, OM, SO 4 2- , and NH 4 + for both end points (even after adjustments of other constituents), among which BC and SO 4 2- showed the strongest associations. All constituents had largely linear C-R relationships in the low exposure range, but most tailed off at higher exposure concentrations. Our findings suggest that long-term exposure to PM 2.5 is significantly associated with higher rates of incident dementia and AD and that SO 4 2- , BC, and OM related to traffic and fossil fuel combustion might drive the observed associations.

Particulate sulfate is one of the most important components in the atmosphere. The observation of rapid sulfate aerosol production during haze events provoked scientific interest in the multiphase oxidation of SO 2 in aqueous aerosol particles. Diverse oxidation pathways can be enhanced or suppressed under different aerosol acidity levels and high ionic strength conditions of atmospheric aerosol. The importance of ionic strength to sulfate multiphase chemistry has been verified under laboratory conditions, though studies in the actual atmosphere are still limited. By utilizing online observations and developing an improved solute strength-dependent chemical thermodynamics and kinetics model (EF-T&K model, EF is the enhancement factor that represents the effect of ionic strength on an aerosol aqueous-phase reaction), we provided quantitative evidence that the H 2 O 2 pathway was enhanced nearly 100 times and dominated sulfate formation for entire years (66%) in Tianjin (a northern city in China). TMI (oxygen catalyzed by transition-metal ions) (14%) and NO 2 (14%) pathways got the second-highest contributions. Machine learning supported the result that aerosol sulfate production was more affected by the H 2 O 2 pathway. The collaborative effects of atmospheric oxidants and SO 2 on sulfate aerosol production were further investigated using the improved EF-T&K model. Our findings highlight the effectiveness of adopting target oxidant control as a new direction for sustainable mitigation of sulfate, given the already low SO 2 concentrations in China.

India is home to 1.3 billion people who are exposed to some of the highest levels of ambient air pollution in the world. In addition, India is one of the fastest-growing carbon-emitting countries. Here, we assess how two strategies to reuse waste-heat from coal-fired power plants and other large sources would impact PM 2.5 -air quality, human health, and CO 2 emissions in 2015 and a future year, 2050, using varying levels of policy adoption (current regulations, proposed single-sector policies, and ambitious single-sector strategies). We find that power plant and industrial waste-heat reuse as input to district heating systems (DHSs), a novel, multisector strategy to reduce local biomass burning for heating emissions, can offset 71.3-85.2% of residential heating demand in communities near a power plant (9.3-12.4% of the nationwide heating demand) with the highest benefits observed during winter months in areas with collocated industrial activity and higher residential heating demands (e.g., New Delhi). Utilizing waste-heat to generate electricity via organic Rankine cycles (ORCs) can generate an additional 22 (11% of total coal-fired generating capacity), 41 (8%), 32 (13%), and 6 (5%) GW of electricity capacity in the 2015, 2050-current regulations, 2050-single-sector, and 2050-ambitious-single-sector scenarios, respectively. Emission estimates utilizing these strategies were input to the GEOS-Chem model, and population-weighted, simulated PM 2.5 showed small improvements in the DHS (0.2-0.4%) and ORC (0.3-3.4%) scenarios, where the minimal DHS PM 2.5 -benefit is attributed to the small contribution of biomass burning for heating to nationwide PM 2.5 emissions (much of the biomass burning activity is for cooking). The PM 2.5 reductions lead to ∼130-36,000 mortalities per year avoided among the scenarios, with the largest health benefits observed in the ORC scenarios. Nationwide CO 2 emissions reduced <0.04% by DHSs but showed larger reductions using ORCs (1.9-7.4%). Coal fly-ash as material exchange in cement and brick production was assessed, and capacity exists to completely reutilize unused fly-ash toward cement and brick production in each of the scenarios.

Ozone (O 3 ) isopleths describe the nonlinear responses of O 3 concentrations to changes in nitrogen oxides (NO X ) and volatile organic compounds (VOCs) and thus are pivotal to the determination of O 3 control requirements. In this study, we innovatively use the Community Multiscale Air Quality model with the high-order decoupled direct method (CMAQ-HDDM) to simulate O 3 pollution of China in 2017 and derive O 3 isopleths for individual cities. Our simulation covering the entire China Mainland suggests severe O 3 pollution as 97% of the residents experienced at least 1 day, in 2017, in excess of Chinese Level-II Ambient Air Quality Standards for O 3 as 160 μg·m -3 (81.5 ppbV equally). The O 3 responses to emissions of precursors vary widely across individual cities. Densely populated metropolitan areas such as Jing-Jin-Ji, Yangtze River Delta, and Pearl River Delta are following NO X -saturated regimes, where a small amount of NO X reduction increases O 3 . Ambient O 3 pollution in the eastern region generally is limited by VOCs, while in the west by NO X . The city-specific O 3 isopleths generated in this study are instrumental in forming hybrid and differentiated strategies for O 3 abatement in China.

The COVID-19 quarantine in China is thought to have reduced ambient air pollution. The overall exposure of the population also depends, however, on indoor air quality and human mobility and activities. Here, by integrating real-time mobility data and a questionnaire survey on time-activity patterns during the pandemic, we show that despite a decrease in ambient PM 2.5 during the quarantine, the total population-weighted exposure to PM 2.5 considering both indoor and outdoor environments increased by 5.7 μg m -3 (95% confidence interval, 1.2-11.0 μg m -3 ). The increase in population-weighted exposure was mainly driven by a nationwide urban-to-rural population migration before the Spring Festival coupled with the freezing of the migration backward due to the quarantine, which increased household energy consumption and the fraction of people exposed to rural household air pollution indoors. Our analysis reveals an increased inequality of air pollution exposure during the quarantine and highlights the importance of household air pollution for population health in China.

Because of the severe air pollution in northern China, facemasks have gained popularity in this area in recent years. Although the results of previous studies have shown the effectiveness of wearing facemasks for intercepting particles, the individual differences and the overall health benefits of wearing facemasks have not been comprehensively documented. In this study, using both model and personal tests under various conditions, we test eight major brands of facemasks for their removal efficiencies (REs) of particulate matter (PM) in six size ranges (from 0.3 μm to >10 μm). The results are used to assess the overall exposure reduction at the individual and population levels in Beijing. We find significant differences in REs among PM sizes, facemask brands, pollution levels, and genders. Combining the information on the usage of various brands, facemask wearing rates, and PM 2.5 concentrations in the ambient and indoor air in this area, we evaluate the overall effect of facemask wearing on PM 2.5 exposure reduction. It is quantitatively demonstrated that because people spend most time indoors, facemask protection is limited. For facemask wearers, the overall exposure can be reduced by less than 20%, whereas the reduction rate is as low as 2.4 ± 1.6% for the entire adult populations even in the year with the highest level of pollution with an annual mean PM 2.5 concentration of 102 ± 98 μg∙m -3 . As a strategy of self-protection from long-term exposure to particulate matter, wearing facemasks outdoors is inferior to the installation of indoor air purifiers., (Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Ammonium is one of the dominant inorganic water-soluble ions in fine particulate matter (PM 2.5 ). In this study, source apportionment and thermodynamic equilibrium models were used to analyze the relationship between pH and the partitioning of ammonium (ε(NH 4 )) using hourly ambient samples collected from Tianjin, China. We found a "Reversed-S curve" between pH and ε(NH + )) using hourly ambient samples collected from Tianjin, China. We found a "Reversed-S curve" between pH and ε(NH 4 + ) from the ambient hourly aerosol dataset when the theoretical ε(NO 3 - ) * (an index identified in this work) was within specific ranges. A Boltzmann function was then used to fit the Reversed-S curve. For the summer data set, when ε(NO 3 - ) * was between 0.7 and 0.8, the fitted R 2 was 0.88. Through thermodynamic analysis, we found that the values of k [H + ] 2 ( k = 3.08 × 10 4 L 2 mol -2 ) and ε(NO 3 - ) * can influence the pH-ε(NH 4 + ) curve. Under certain situations, the values of k [H + ] 2 and ε(NO 3 - ) * are similar to each other, and ε(NH 4 + ) is sensitive to pH, suggesting that ε(NO 3 - ) * plays an important role in affecting the ε(NH 4 + ). During summer, winter, and spring seasons, when the relative humidity was greater than 0.36 and ε(NO 3 - ) * was between 0.8 and 0.95, there was an obvious Reversed-S curve, with R 2 = 0.60. The theoretical k [H + ] 2 and ε(NO 3 - ) * developed in this work can be used to analyze the gas-particle partitioning of ammonia-ammonium and nitrate-nitric acid in the ambient atmosphere. Also, it is the first time that we created the joint source-NH 3 /HNO 3 maps to integrate sources, aerosol pH and liquid water content, and ions (altogether in one map), which can provide useful information for designing effective strategies to control particulate matter pollution.

In addition to many recent actions taken to reduce emissions from energy production, industry, and transportation, a new campaign substituting residential solid fuels with electricity or natural gas has been launched in Beijing, Tianjin, and 26 other municipalities in northern China, aiming at solving severe ambient air pollution in the region. Quantitative analysis shows that the campaign can accelerate residential energy transition significantly, and if the planned target can be achieved, more than 60% of households are projected to remove solid fuels by 2021, compared with fewer than 20% without the campaign. Emissions of major air pollutants will be reduced substantially. With 60% substitution realized, emission of primary PM 2.5 and contribution to ambient PM 2.5 concentration in 2021 are projected to be 30% and 41% of those without the campaign. With 60% substitution, average indoor PM 2.5 concentrations in living rooms in winter are projected to be reduced from 209 (190 to 230) μg/m 3 to 125 (99 to 150) μg/m 3 The population-weighted PM 2.5 concentrations can be reduced from 140 μg/m 3 in 2014 to 78 μg/m 3 or 61 μg/m 3 in 2021 given that 60% or 100% substitution can be accomplished. Although the original focus of the campaign was to address ambient air quality, exposure reduction comes more from improved indoor air quality because ∼90% of daily exposure of the rural population is attributable to indoor air pollution. Women benefit more than men., Competing Interests: The authors declare no conflict of interest.

In this work, we utilize a rich set of simulated and ground-based observational data in Tianjin, China to examine and compare the differences in aerosol acidity and composition predicted by three popular thermodynamic equilibrium models: ISORROPIA II, the Extended Aerosol Inorganics Model vision IV (E-AIM IV), and the Aerosol Inorganic-Organic Mixtures Functional groups Activity Coefficients model (AIOMFAC). The species used to estimate aerosol acidity for both simulated and ambient data were NH 4 + , Na + , SO 4 2- , NO 3 - , and Cl - . For simulated data, there is good agreement between ISORROPIA II and E-AIM IV predicted acidity in the forward and metastable mode, resulting from the hydrogen ion activity coefficient (γ (H + ) ) and the molality ( m (H + ) ) showing opposite trends. While almost all other inorganic species concentrations are found to be similar among the three models, such is not the case for the bisulfate ion (HSO 4 - ), which is linked to m (H + ) . We find that differences in predicted bisulfate between the three models primarily result from differences in the treatment of the HSO 4 - ↔ H + + SO 4 2- reaction for highly acidic conditions. This difference in bisulfate is responsible for much of the difference in estimated pH for the ambient data (average pH of 3.5 for ISORROPIA II and 3.0 for E-AIM IV).

Atmospheric chemical transport models (CTMs) have been widely used to simulate spatiotemporally resolved PM 2.5 concentrations. However, CTM results are usually prone to bias and errors. In this study, we improved the accuracy of PM 2.5 predictions by developing an ensemble deep learning framework to fuse model simulations with ground-level observations. The framework encompasses four machine-learning models, i.e., general linear model, fully connected neural network, random forest, and gradient boosting machine, and combines them by stacking approach. This framework is applied to PM 2.5 concentrations simulated by the Community Multiscale Air Quality (CMAQ) model for China from 2014 to 2017, which has complete spatial coverage over the entirety of China at a 12-km resolution, with no sampling biases. The fused PM 2.5 concentration fields were evaluated by comparing with an independent network of observations. The R 2 values increased from 0.39 to 0.64, and the RMSE values decreased from 33.7 μg/m 3 to 24.8 μg/m 3 . According to the fused data, the percentage of Chinese population residing under the level II National Ambient Air Quality Standards of 35 μg/m 3 for PM 2.5 has increased from 46.5% in 2014 to 61.7% in 2017. The method is readily adapted to utilize near-real-time observations for operational analyses and forecasting of pollutant concentrations and can be extended to provide source apportionment forecasts as well.

Nitrate is one of the most abundant inorganic water-soluble ions in fine particulate matter (PM 2.5 ). However, the formation mechanism of nitrate in the ambient atmosphere, especially the impacts of its semivolatility and the various existing forms of nitrogen, remain under-investigated. In this study, hourly ambient observations of speciated PM 2.5 components (NO 3 - , etc.) were collected in Tianjin, China. Source contributions were analyzed by PMF/ME2 (Positive Matrix Factorization using the Multilinear Engine 2) program, and pH were estimated by ISORROPIA-II, to investigate the relationship between pH and nitrate. Five sources (factors) were resolved: secondary sulfate (SS), secondary nitrate (SN), dust, vehicle and coal combustion. SN and pH showed a triangle-shaped relationship. When SS was high, the fraction of nitrate partitioning into the aerosol phase exhibits a characteristic "S-curve" relationship with pH for different seasons. An index ( I 4 2- , etc.) were collected in Tianjin, China. Source contributions were analyzed by PMF/ME2 (Positive Matrix Factorization using the Multilinear Engine 2) program, and pH were estimated by ISORROPIA-II, to investigate the relationship between pH and nitrate. Five sources (factors) were resolved: secondary sulfate (SS), secondary nitrate (SN), dust, vehicle and coal combustion. SN and pH showed a triangle-shaped relationship. When SS was high, the fraction of nitrate partitioning into the aerosol phase exhibits a characteristic "S-curve" relationship with pH for different seasons. An index ( I TL ) is developed and combined with pH to explore the sensitive regions of "S-curve". Controlling the emissions of anions (SO 4 2- , Cl - ), cations (Ca 2+ , Mg 2+ , etc.) and gases (NO x , NH 3 , SO 2 , etc.) will change pH, potentially reducing or increasing SN. The findings of this work provide an effective approach for exploring the formation mechanisms of nitrate under different influencing factors (sources, pH, and I RL ).

In China, rural migrant workers (RMWs) are employed in urban workplaces but receive minimal resources and welfare. Their residential energy use mix (REM) and pollutant emission profiles are different from those of traditional urban (URs) and rural residents (RRs). Their migration towards urban areas plays an important role in shaping the magnitudes and spatial patterns of pollutant emissions, ambient PM 2.5 (fine particulate matter with a diameter smaller than 2.5 μm) concentrations, and associated health impacts in both urban and rural areas. Here we evaluate the impacts of RMW migration on REM pollutant emissions, ambient PM 2.5 , and subsequent premature deaths across China. At the national scale, RMW migration benefits ambient air quality because RMWs tend to transition to a cleaner REM upon arrival at urban areas-though not as clean as urban residents'. In 2010, RMW migration led to a decrease of 1.5 μg/m 3 in ambient PM 2.5 exposure concentrations (C ex ) averaged across China and a subsequent decrease of 12,200 (5700 to 16,300, as 90% confidence interval) in premature deaths from exposure to ambient PM 2.5 . Despite the overall health benefit, large-scale cross-province migration increased megacities' PM 2.5 levels by as much as 10 μg/m 3 due to massive RMW inflows. Model simulations show that upgrading within-city RMWs' REMs can effectively offset the RMW-induced PM 2.5 increase in megacities, and that policies that properly navigate migration directions may have potential for balancing the economic growth against ambient air quality deterioration. Our study indicates the urgency of considering air pollution impacts into migration-related policy formation in the context of rapid urbanization in China., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Both particulate matter (PM) and black carbon (BC) impact climate change and human health. Uncertainties in emission inventories of PM and BC are partially due to large variation of measured emission factors (EFs) and lack of EFs from developing countries. Although there is a debate whether thermal-optically measured elemental carbon (EC) may be referred to as BC, EC is often treated as the same mass of BC. In this study, EFs of PM (EF(PM)) and EC (EF(EC)) for 9 crop residues and 5 coals were measured in actual rural cooking and coal stoves using the carbon mass balance method. The dependence of the EFs on fuel properties and combustion conditions was investigated. It was found that the mean EF(PM) were 8.19 ± 4.27 and 3.17 ± 4.67 g/kg and the mean EF(EC) were 1.38 ± 0.70 and 0.23 ± 0.36 g/kg for crop residues and coals, respectively. PM with size less than 10 μm (PM(10)) from crop residues were dominated by particles of aerodynamic size ranging from 0.7 to 2.1 μm, while the most abundant size ranges of PM(10) from coals were either from 0.7 to 2.1 μm or less than 0.7 μm. Of various fuel properties and combustion conditions tested, fuel moisture and modified combustion efficiency (MCE) were the most critical factors affecting EF(PM) and EF(EC) for crop residues. For coal combustion, EF(PM) were primarily affected by MCE and volatile matter, whereas EF(EC) were significantly influenced by ash content, volatile matter, heat value, and MCE. It was also found that EC emissions were significantly correlated with emissions of PM with size less than 0.4 μm.