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Traditional discharge monitoring usually relies on measuring flow velocity and cross-section area with various velocimeters or remote-sensing approaches. However, the topography of mountain streams in remote sites largely hinders the applicability of velocity-area methods. We here present a method to continuously monitor mountain stream discharge using a low-cost commercial camera and deep learning algorithm. A procedure of automated image categorization and discharge classification was developed to extract information on flow patterns and volumes from high-frequency red–green–blue (RGB) images with deep convolutional neural networks (CNNs). The method was tested at a small, steep, natural stream reach in southern China. Reference discharge data was acquired from a V-shaped weir and ultrasonic flowmeter installed a few meters downstream of the camera system. Results show that the discharge-relevant stream features implicitly embedded in RGB information can be effectively recognized and retrieved by CNN to achieve satisfactory accuracy in discharge measurement. Coupling CNN and traditional machine learning models (e.g., support vector machine and random forest) can potentially synthesize individual models’ diverse merits and improve generalization performance. Besides, proper image pre-processing and categorization are critical for enhancing the robustness and applicability of the method under environmental disturbances (e.g., weather and vegetation on river banks). Our study highlights the usefulness of deep learning in analyzing complex flow images and tracking flow changes over time, which provides a reliable and flexible alternative apparatus for continuous discharge monitoring of rocky mountain streams.

We apply a Local Ensemble Transform Kalman Smoother (LETKS) in combination with the global aerosol climate model ECHAM-HAM to estimate aerosol emissions from POLDER-3/PARASOL observations for the year 2006. We assimilate Aerosol Optical Depth at 550 mnm (AOD550), Ångström Exponent for 550 nm and 865 nm (AE550-865) and Single Scattering Albedo at 550 nm (SSA550) in order to improve modeled aerosol mass, size and absorption simultaneously. The new global aerosol emissions increase to 1419 Tg·yr-1 (+28 %) for dust, 1850 Tg·yr-1 (+75 %) for sea salt, 215 Tg·yr-1 (+143 %) for organic aerosol and 13.3 Tg·yr-1 (+75 %) for black carbon, while the sulfur dioxide emissions increase to 198 Tg·yr-1 (+42 %) and total deposition of sulfates to 293 Tg·yr-1 (+39 %). Organic and black carbon emissions are much higher than their prior values from bottom up inventories with a stronger increase in biomass burning sources (+193 % and +90 %) than in anthropogenic sources (115 % and 70 %). The evaluation of the experiments with POLDER (assimilated) and AERONET as well as MODIS Dark Target (independent) observations shows a clear improvement compared to the ECHAM-HAM control run. Specifically based on AERONET the global mean error of AOD550 improves from -0.094 to -0.006 while AAOD550 improves from -0.009 to -0.004 after the assimilation. A smaller improvement is observed also in AE550-865 mean absolute error (from 0.428 to 0.393), with a considerably higher improvement over isolated island sites over the ocean. The new dust emissions are closer to the ensemble median of AEROCOM I, AEROCOM III and CMIP5 as well as some of the previous assimilation studies. The new sea salt emissions get closer to the reported emissions from previous studies. Indications of a missing fraction of coarse dust and sea salt particles are discussed. The biomass burning changes (based on POLDER) can be used as alternative biomass burning scaling factors for the GFAS inventory distinctively estimated for organic carbon (2.93) and black carbon (1.90), instead of the recommended scaling of 3.4 (Kaiser et al. 2012). The estimated emissions are highly sensitive to the relative humidity due to aerosol water uptake, especially in the case of the sulfates. We found that ECHAM-HAM, like most of the GCMs that participated in AEROCOM and CMIP6, overestimated the relative humidity compared to ERA-5 and as a result the water uptake by aerosols, assuming the kappa values are not underestimated. If we use the ERA-5 relative humidity, sulfate emissions must be further increased, as modeled sulfate AOD is lowered. Specifically, over East Asia, the lower AOD can be attributed to the underestimated precipitation and the lack of simulated nitrates in the model.

Data for the manuscript: "Aerosol emissions estimation with POLDER" atAtmospheric Chemistry and Physics. Thefiles include the yearly ensemble mean of all experiments (CTL_ECHAM, DAS_ECHAM, CTL_ERA5, DAS_ERA5, RES_LOW) for most variables produced by the model. In addition the Aerosol Optical Depth at 550nm (AOD550) and at 865nm (AOD865) along with the Absorption Aerosol Optical Depth at 550nm (ABS550) are provided in 3-hourly temporal resolution for all experiments and for POLDER.

Summary Exposure to air pollution by burning solid fuels (such as coal) for residential cooking and heating in China has caused significant health impacts in the past. The government has implemented measures to replace coal with relatively clean energy sources (e.g., natural gas). However, the scale and scope of health benefits associated with such energy switching in an urban context, when considering both ambient and indoor air quality, remain unclear. Here we used an atmospheric chemical transport model showing that relatively clean energy use increased from 2% to 71% from 1980 to 2014, and although annual premature deaths attributed to particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) from urban residential sources increased from 136,000 (87,000–194,000) to 202,000 (117,000–302,000) during the same period, this was primarily due to population growth, urbanization, aging, and background mortality rate changes. In the absence of energy switching, there would have been 2.2 million additional premature deaths. The results provide solid evidence on health benefits of energy switching, suggesting further switching to cleaner energy for expanded health-climate co-benefits.

Air pollution is one of the most concerning environmental threats to human health [...]

Harmful SO2 largely originates from coal and oil combustions, but in some areas the biomass burning contribution could not be ignored. Here, we evaluated SO2 emissions from biomass burning (BB-SO2) with largely focusing on regional difference and temporal trends in the relative contributions of biomass burning from different sectors. Globally, the biomass burning emitted 4.26 (3.20–6.20) Tg SO2 in 2014, contributing 4.0% of the total SO2 emissions stemming from anthropogenic sources and natural open fires. But in some African and South Asian countries, biomass burning was a major source of SO2 with the contribution as high as 80–90%. Regarding sector contributions of biomass SO2, open fires contributed nearly half, followed by the residential sector (~29%) on the global scale, however, substantially different profiles were revealed across countries. Residential sector is the largest anthropogenic BB-SO2 source in the developing countries, while in the developed countries, industry and energy production were the two main anthropogenic BB-SO2sources. From 1960 to 2014, biomass SO2 emission, either the absolute amount or the relative contribution to the total, increased in the U.S. and Europe, and the contributions were over 20% in some countries. The biomass burning SO2 emission showed an increasing trend in India and a unimodal change in China, while a decreasing trend in the relative contributions were revealed in these two largest developing countries, which were 2.7% and 0.8%, respectively in 2014. With unignorable biomass burning contribution to SO2, as well as other hazardous air pollutants, in some regions, it is suggested that in assessing climate and health impacts of promoted biomass utilization when phasing out of fossil fuels, multiple components should be co-evaluated.

Gaseous and particulate chlorine species play an important role in modulating tropospheric oxidation capacity, aerosol water uptake, visibility degradation, and human health. The lack of recent global continental chlorine emissions has hindered modeling studies of the role of chlorine in the atmosphere. Here, we develop a comprehensive global emission inventory of gaseous HCl and particulate Cl-(pCl), including 35 sources categorized in six source sectors based on published up-to-date activity data and emission factors. These emissions are gridded at a spatial resolution of 0.1° × 0.1° for the years 1960 to 2014. The estimated emissions of HCl and pCl in 2014 are 2354 (1661-3201) and 2321 (930-3264) Gg Cl a-1, respectively. Emissions of HCl are mostly from open waste burning (38%), open biomass burning (19%), energy (19%), and residential (13%) sectors, and the major sources classified by fuel type are combustion of waste (43%), biomass (32%), and coal (25%). Emissions of pCl are mostly from biofuel (29%) and open biomass burning processes (44%). The sectoral and spatial distributions of HCl and pCl emissions are very heterogeneous along the study period, and the temporal trends are mainly driven by the changes in emission factors, energy intensity, economy, and population.

Residential heating using solid fuels contributes significantly to air pollution and has subsequent health impacts in China. To mitigate emissions, a clean heating campaign (CHC-1) covering 28 municipalities has been implemented. Although only a single penetration rate was initially planned by CHC-1 for all municipalities, outcomes in the different municipalities varied considerably. Recently, a second phase (CHC-2) has been launched for the remaining 128 municipalities in northern China with once again a fixed penetration rate set. Here, we quantified factors that affected the penetration rates of CHC-1, developed an intervention scheme with differentiated targets for CHC-2, and compared the environmental and health benefits of the fixed- and differentiated-rate strategies. We found that the penetration rates of CHC-1 depended on per capita income, terrain slope, and population density and that such relationships could be quantified using a piecewise regression model. This model was applied to develop a differentiated-rate strategy for CHC-2. It clearly evidenced that a differentiated scheme would be more environmentally beneficial. Although the same number of rural households can achieve clean heating under both intervention scenarios, the proposed differentiated strategy can prevent 30 000 (23 000-34 000) premature deaths associated with residential heating annually compared to the 26 000 (21 000-31 000) premature deaths prevented under the fixed-rate scheme. Differences among gender and age groups and the effects of urbanization and aging are also discussed.

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.

Fossil fuel combustion is the dominant source of global sulfur dioxide (SO2) emissions. With rapid globalization, the expansion of international fuel trade may have profound impacts on SO2 emission...

Atmospheric black carbon (BC) concentrations are governed by both emissions and meteorological conditions. Distinguishing these effects enables quantification of the effectiveness of emission mitigation actions by excluding meteorological effects. Here, we develop reduced-form models in both direct (RFDMs) and inverse (RFIMs) modes to estimate ambient BC concentrations. The models were developed based on outputs from multiyear simulations under three conditional scenarios with realistic or fixed emissions and meteorological conditions. We established a set of probabilistic functions (PFs) to quantify the meteorological influences. A significant two-way linear relationship between multiyear annual emissions and mean ambient BC concentrations was revealed at the grid cell scale. The correlation between them was more significant at grid cells with high emission densities. The concentrations and emissions at a given grid cell are also significantly correlated with emissions and concentrations of the surrounding areas, respectively, although to a lesser extent. These dependences are anisotropic depending on the prevailing winds and source regions. The meteorologically induced variation at the monthly scale was significantly higher than that at the annual scale. Of the major meteorological parameters, wind vectors, temperature, and relative humidity were found to most significantly affect variation in ambient BC concentrations.

Exposure to household air pollution from solid fuel use is a leading environmental risk factor causing millions of premature deaths, mostly in developing countries. Health hazards in particulate ma...

Historically, beehive coke ovens (BCOs) were extensively operated in China and emitted large quantities of pollutants, including primary PM2.5 and secondary PM2.5 precursors, and other climate forc...

Summary Fine-particulate-matter pollution (PM2.5) is linked to millions of deaths annually. Consumption in one region can cause production and PM2.5 emissions in another, but the socioeconomic drivers of transboundary PM2.5 emissions are not fully understood. Here, we quantify the driving factors of primary-particle and oxidized-precursor emissions from 2004 to 2011 at global and regional scales. The results indicate that the growth rate of global PM2.5 emissions slowed from 2004 to 2011, mainly due to improvements in energy intensities and production efficiency. Improvements in the emission intensity of coal represent the main factor offsetting SO2 and NOx emissions, whereas emission controls implemented for industrial processes have largely contributed to reducing primary PM2.5 emissions. The net emissions embodied in East Asia's exports to developed countries declined, while that to developing countries increased rapidly. The analysis creates opportunities to involve both producers and consumers in co-mitigation of various pollutants.

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., Residential solid fuel use constitutes a large amount of air pollution but has been gradually replaced by other cleaner energy during the past three decades. Here the authors investigated the contribution of rural residential sector to ambient PM2.5 pollution and the resulting climate forcing and health impacts, and find that the remaining large quantities of solid fuels used in rural households are still a major contributor to ambient air pollution despite of decrease in its pollutant emissions and relative contribution to PM2.5 due to the clean energy transition.

Absorbing aerosol has the capacity to warm the climate, but the amount of warming is highly uncertain. AAOD (Absorptive Aeorosl Optical Depth) is an optical measure of the abundance of this absorbing aerosol, comprising mineral dust, black and brown carbon and can be retrieved from satellite measurements providing an almost global view on absorbing aerosol.In this study we evaluate AEROCOM models with satellite observations of AAOD and SSA (Single Scattering Albedo) and interpret the discrepancies. Over source regions, diversity in model AAOD is mostly due to emissions even though models employ different assumptions regarding the imaginary refractive index. On the one hand this suggests emissions to be a major error source, on the other hand it suggests that the AEROCOM ensemble as a whole may have a bias with regards to MAC (Mass Absorption Coefficient). We show that in the models AAOD scales almost linearly with emissions (either black carbon or dust) and this allows the use of observations as a constraint. In contrast, model diversity in AOD is shown to depend in almost equal measure on emissions, lifetimes and MECs (Mass Extinction Coefficient). We also analyse mineral dust and black carbon lifetimes by considering the contrast in AAOD over source regions and over outflow regions, and again provide observations constraints.While the older Phase II models generally underestimate AAOD, Phase III models tend to straddle the observations, with some models over-estimating and other models underestimating AAOD. Emissions seem to be the driving factor in this difference. The amount of diversity is larger in the Phase III than Phase II models.This study was conducted using four satellite datasets of AAOD and SSA. These datasets were extensively evaluated with AERONET. Dearth of observations prevents global assesment of the satellite retrievals. However, we show that model evaluation is relatively independent of the chosen dataset, even though we identify significant biases between the datasets.

Biomass burning (BB) injects aerosols into the atmosphere and can thereby affect the earth climate and human health. Yet the modeling of BB aerosols exhibits significant bias. Here we present a comprehensive evaluation of AeroCom model simulations with satellite observations to understand such uncertainties. A total of 59 model runs using 17 models from three AeroCcom Phase III experiments (i.e., Biomass Burning emissions, CTRL2016, and CTRL2019 experiment) and 14 satellite products are involved. AOD (aerosol optical depth) at 550 nm wavelength during the fire season over three typical fire regions (Amazon, South Hemisphere Africa, and Boreal North America, or AMAZ, SHAF, and BONA) is the focus of our study, although we also consider AE and SSA from POLDER.The 14 satellite products are shown to have quite substantial differences from AERONET observation. But we show that such differences have little impact on the model evaluation which is mainly affected by modeling bias. Through the comparison with POLDER observation, we found the modeled AOD are biased by -93% ~ 174% with most models showing significant underestimations even for the most recent modeling experiment (i.e., CTRL19). SHAF is among the three regions with the strongest underestimation in general. By scaling up the input emissions, such negative bias would be significantly reduced, which, however, has little impact on the day-to-day correlation between models and observations.On top of the satellite-based model evaluation, we interpret the model diversity from the aspect of aerosol emissions, lifetime, and MEC (mass extinction coefficient), which are further linked with specific parameters in models. These three parameters cause similar levels of AOD diversity, which is quite different from the modeled aerosols during non-fire season when the contribution of lifetime is predominant. During the fire season, diversity caused by lifetime is strongly affected by local deposition; as a matter of fact, models tend to do quite poorly in simulating precipitation strength. Modeled MECs show significant correlations with aerosol wet-growth (which is known to be challenging to models) and AE (Angstrom Exponent) for some involved models. Comparisons with POLDER observed AE suggests some models tend to underestimate AE and thus MEC, which might be responsible for the overall AOD underestimation in certain models. Additionally, we show that model AOD biases correlate with satellite observed formaldehyde columns, suggesting SOA formation may be insufficiently captured by models.

Residential solid fuel combustion is a major emission source of PAHs (polycyclic aromatic hydrocarbons) in most developing countries, including China; however, accurate estimates of PAH emissions are often challenged by limited real-world emission factors (EFs) under field conditions, which can hardly be repeated in laboratory-controlled tests. In this study, a series of field measurements was conducted to determine the emissions of 28 PAHs from different fuel-stove combinations. A total of 14 fuel-stove combinations were studied. The total EFs of 28 PAHs (EFPAH28), on the basis of fuel mass, ranged from 20.7 to 535 mg/kg, with relatively lower EFs for coal than for biomass. Biomass burning in gasifier stoves had lower PAH EFs and fewer toxic PAH species than biomass burning in traditional brick stoves. Fuel type was a significant factor affecting PAH emissions, while stove difference had a relatively smaller influence. Much higher EFs were found from these field tests than from the idealized laboratory tests, which indicated significant underestimation in inventories based on the laboratory-based EFs. Biomass and coal had different profiles, with larger intra-fuel variations in coal than those in biomass. Highly variable values of some, though not all, commonly used isomer ratios indicated substantial biases in source apportionment relying on single or simple ratios without correction, and the MCE was found to be significantly corrected with some ratios.

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 other 26 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 less than 20% without the campaign. Emissions of major air pollutants will be reduced substantially. With 60% substitution realized, emission of primary PM2.5 and contribution to ambient PM2.5 concentration in 2021 are projected to be 30% and 41% of those without the campaign. With 60% substitution, average indoor PM2.5 concentrations in living rooms in winter are projected to be reduced from 209 (190-230) μg/m3 to 125 (99-150) μg/m3. The population-weighted PM2.5 concentrations can be reduced from 140 μg/m3 in 2014 to 78 μg/m3 or 61 μg/m3 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 approximately 90% of daily exposure of the population is attributable to indoor air pollution. Women benefit more than men.

Residential contributions are substantially magnified from energy use, emissions, air pollution, to health impacts., Residential contribution to air pollution–associated health impacts is critical, but inadequately addressed because of data gaps. Here, we fully model the effects of residential energy use on emissions, outdoor and indoor PM2.5 concentrations, exposure, and premature deaths using updated energy data. We show that the residential sector contributed only 7.5% of total energy consumption but contributed 27% of primary PM2.5 emissions; 23 and 71% of the outdoor and indoor PM2.5 concentrations, respectively; 68% of PM2.5 exposure; and 67% of PM2.5-induced premature deaths in 2014 in China, with a progressive order of magnitude increase from sources to receptors. Biomass fuels and coal provided similar contributions to health impacts. These findings are particularly true for rural populations, which contribute more to emissions and face higher premature death risks than urban populations. The impacts of both residential and nonresidential emissions are interconnected, and efforts are necessary to simultaneously mitigate both emission types.

Household air pollution is one important environmental risk to human health in China, however, rural air pollution is often underappreciated and field measurements are limited compared to studies in urban area. The present study investigated indoor and outdoor polycyclic aromatic hydrocarbons (PAHs), and population inhalation exposure from two rural counties in Shanxi and Guizhou provinces, respectively. The indoor levels were higher than the corresponding outdoor concentration in both sites, which was mainly affected by the indoor fuel combustion emissions. A majority of particulate PAHs was found in submicron PM1.0. Fuel types had significant impacts on the total PAHs mass concentration but also composition profiles. In homes using electricity, the PAHs concentration was lower than those burning solid fuels, and there were relatively more fractions of low molecular weight PAHs with 2–3 rings, while for those burning solid fuels, higher fractions of high molecular weight PAHs with 5–6 rings may pose larger adverse health impacts. Although the daily inhalation exposure level for the population using electricity was much lower than those using solid fuels, the estimated Incremental Lifetime Cancer Risk still exceeded the deemed safe risk of 10−6. Inhalation exposure amounts measured by using portable carried samplers were compared to the estimation based on area concentration. The substantial discrepancy between them confirmed a preferable use of portable carried samplers in exposure evaluation and health risk assessment.

Rural household energy use for cooking and heating is an important source of air pollutants in China, as it affects both human health and climate change. However, the magnitude of rural household energy use, especially during the recent rapid socioeconomic transition period, has not been well quantified. Here, we present first-hand nationwide data from a 34,489-household energy-mix survey and a 1,670-household fuel-weighing campaign. We found that the consumption of wood and crop residues in rural China decreased by 63% and 51%, respectively, from 1992 to 2012, and these decreases were much greater than the 15% and 8%, respectively, reported by the International Energy Agency and Food and Agriculture Organization. The rapid residential energy transition over these two decades was primarily driven by the rapid socioeconomic development. One important implication of this transition is the significant reduction in the emissions of major air pollutants, especially incomplete combustion products leading to significant impacts on health and climate. Use of wood and crop residue for cooking and heating in rural China is a significant source of carbon emissions and air pollution. Using a survey of more than 34,000 households, researchers show that between 1992 and 2012 usage of these fuels decreased by much more than previous estimates, due primarily to rising incomes.

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 PM2.5 (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 PM2.5, 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/m3 in ambient PM2.5 exposure concentrations (Cex) 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 PM2.5. Despite the overall health benefit, large-scale cross-province migration increased megacities' PM2.5 levels by as much as 10 μg/m3 due to massive RMW inflows. Model simulations show that upgrading within-city RMWs' REMs can effectively offset the RMW-induced PM2.5 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. Keywords: Rural migrant works, Urbanization in China, Ambient air pollution, Particulate matter, Human health

Air pollutant emissions, fuel consumption, and household air pollution were investigated in rural Hubei, central China, as a revisited evaluation of an intervention program to replace coal use by wood in gasifier stoves. Measured emission factors were comparable to the results measured two years ago when the program was initiated. Coal combustion produced significantly higher emissions of CO2, CH4, and SO2 compared with wood combustion; however, wood combustion in gasifier stoves had higher emissions of primary PM2.5 (particles with diameter less than 2.5 μm), Elemental Carbon (EC) and Organic Carbon (OC). In terms of potential impacts on climate, although the use of wood in gasifier stoves produced more black carbon (6.37 vs 910 gCO2e per day per capita from coal and wood use) and less SO2 (-684 vs -312), obvious benefits could be obtained owing to greater OC emissions (-15.4 vs -431), fewer CH4 emissions (865 vs 409) and, moreover, a reduction of CO2 emissions. The total GWC100 (Global Warming Potential over a time horizon of 100 years) would decrease by approximately 90% if coal use were replaced with renewable wood burned in gasifier stoves. However, similar levels of ambient particles and higher indoor OC and EC were found at homes using wood gasifier stoves compared to the coal-use homes. This suggests critical investigations on potential health impacts from the carbon-reduction intervention program.

Because people spend most of their time indoors, the characterization of indoor air quality is important for exposure assessment. Unfortunately, indoor air data are scarce, leading to a major data gap in risk assessment. In this study, PM2.5 concentrations in both indoor and outdoor air were simultaneously measured using on-line particulate counters in 13 households in Haidian, Beijing for both heating and non-heating seasons. A bimodal distribution of PM2.5 concentrations suggests rapid transitions between polluted and non-polluted situations. The PM2.5 concentrations in indoor and outdoor air varied synchronously, with the indoor variation lagging. The lag time in the heating season was longer than that in the non-heating season. The particle sizes in indoor air were smaller than those in ambient air in the heating season and vice versa in the non-heating season. PM2.5 concentrations in indoor air were generally lower than those in ambient air except when ambient concentrations dropped sharply to very low levels or there were internal emissions from cooking or other activities. The effectiveness of an air cleaner to reduce indoor PM2.5 concentrations was demonstrated. Non-linear regression models were developed to predict indoor air PM2.5 concentrations based on ambient data with lag time incorporated. The models were applied to estimate the overall population exposure to PM2.5 and the health consequences in Haidian. The health impacts would be significantly overestimated without the indoor exposure being taken into consideration, and this bias would increase as the ambient air quality improved in the future.

Emissions from household cookfuels strongly affect both air quality and climate change. China is undergoing a rapid transition of cookfuels due to its rapid development, which has significant consequences for environment and health. Unfortunately, detailed information on this transition is scarce. In this study, the trajectory and geographical variation of the cookfuel transition and the factors affecting it were investigated based on panel data on cookfuel choice from the China Family Panel Studies (CFPS) in 2010 and 2012 covering areas that include more than 90% of the national population and a large fraction of repeated households, thus reducing uncertainty. Over this short period, the proportion of Chinese households cooking with solid fuel dropped rapidly from 50% in 2010 to 39% in 2012; 9% and 18% of the solid fuel-using households in 2010 switched to clean energy in 2012, particularly electricity, in urban and rural areas, respectively, according to CFPS. The major forces driving the cookfuel transition include income, educational level, location, energy price, and fuel accessibility. Although switching from biomass to gas and electricity led to a slight increase in CO 2 emissions, the total residential emissions of CO, BC, OC, PM 2.5 , and Hg decreased by more than 10% from 2010 to 2012. The warming effect of increased CO 2 emissions and reduced OC emissions was outweighed by the cooling effect achieved by the emissions reduction of air pollutants with warming impacts, including CO, BC and CH 4 . Although this rapid transition is highly beneficial, it requires national action to accelerate and expand to a greater proportion of poor populations.

Energy data are often reported on an annual basis. To address the climate and health impacts of greenhouse gases and air pollutants, seasonally resolved emissions inventories are needed. The seasonality of energy consumption is most affected by consumption in the residential sector. In this study, a set of regression models were developed based on temperature-related variables and a series of socioeconomic parameters to quantify global electricity and fuel consumption for the residential sector. The models were evaluated against observations and applied to simulate monthly changes in residential energy consumption and the resultant emissions of air pollutants. Changes in energy consumption are strongly affected by economic prosperity and population growth. Climate change, electricity prices, and urbanization also affect energy use. Climate warming will cause a net increase in electricity consumption and a decrease in fuel consumption by the residential sector. Consequently, emissions of CO2, SO2, and Hg are predicted to decrease, while emissions of incomplete combustion products are expected to increase. These changes vary regionally.

Historically, beehive coke ovens (BCOs) were extensively operated in China and emitted large quantities of pollutants, including primary PM

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.

Indoor biomass burning produces large amounts of small particles and hazardous contaminants leading to severe air pollution and potentially high health risks associated with inhalation exposure. Personal samplers provide more accurate estimates of inhalation exposure. In this study, inhalation exposure to size-segregated particles and particulate polycyclic aromatic hydrocarbons (PAHs) for the biomass user was studied by deploying personal samplers. The study found that daily PM2.5 inhalation exposure level was as high as 121 ± 96 μg/m3, and over 84% was finer PM1.0. For PAHs, the exposure level was 113 ± 188 ng/m3, with over 77% in PM1.0. High molecular weight PAHs with larger toxic potentials enriched in smaller particles resulting in much high risks associated with PAHs inhalation exposure. Indoor exposure contributed to ~80% of the total inhalation exposure as a result of high indoor air pollution and longer residence spent indoor. The highest exposure risk was found for the male smoker who conducted cooking activities at home.

Indoor biomass burning is a major contributor to the emission of PAHs (polycyclic aromatic hydrocarbons) in China. To date, estimates of PAH emissions from the burning of biomass have involved considerable uncertainty, mostly from the lack of real-world measurements of emission factors. In this study, we conducted a comprehensive evaluation on PAH emissions from biomass burning in real-world cooking stoves in three Chinese provinces. PAH emission factors, in both particle- and gas-phase, from 11 fuel-stove combinations were measured and the provincial emissions were estimated based on the measured emission factors and fuel consumption. The measured PAH28 emission factors (including 16 US EPA priority PAHs and 12 non-priority PAHs) ranged from 42 mg/kg to 370 mg/kg, with an order of magnitude difference, which was mostly affected by fuel type. The emission factors measured in this study were generally higher than those reported in laboratory studies and were comparable with field studies. The gas-particle distribution indicated that the absorption of PAHs by organic carbon in particulate matter (PM) was the dominant sorption mechanism in gas-particle distribution. The composition profile was different from previous studies, especially for non-priority PAHs, which are highly toxic and should be given more attention. Following the disparities in composition profiles, our study suggests that source apportionment based on single- or multi-diagnostic ratios may lead to large bias and uncertainties. It appears that the toxicity potential of PAHs in northern China emitted from combustion of crop residues is greater than that in southern China where PAHs are mainly emitted from wood combustion.

In an attempt to reduce uncertainty, global organic carbon (OC) emissions from a total of 70 sources were compiled at 0.1° × 0.1° resolution for 2007 (PKU-OC-2007) and country scale from 1960 to 2009. The compilation took advantage of a new fuel-consumption data product (PKU-Fuel-2007) and a series of newly published emission factors (EFOC) in developing countries. The estimated OC emissions were 32.9 Tg (24.1–50.6 Tg as interquartile range), of which less than one third was anthropogenic in origin. Uncertainty resulted primarily from variations in EFOC. Asia, Africa, and South America had high emissions mainly because of residential biomass fuel burning or wildfires. Per-person OC emission in rural areas was three times that of urban areas because of the relatively high EFOC of residential solid fuels. Temporal trend of anthropogenic OC emissions depended on rural population, and was influenced primarily by residential crop residue and agricultural waste burning. Both the OC/PM2.5 ratio and emission intensity, defined as quantity of OC emissions per unit of fuel consumption for all sources, of anthropogenic OC followed a decreasing trend, indicating continuous improvement in combustion efficiency and control measures.

Although PM2.5 (particulate matter with aerodynamic diameters less than 2.5 μm) in the air originates from emissions, its concentrations are often affected by confounding meteorological effects. Therefore, direct comparisons of PM2.5 concentrations made across two periods, which are commonly used by environmental protection administrations to measure the effectiveness of mitigation efforts, can be misleading. Here, we developed a two-step method to distinguish the significance of emissions and meteorological factors and assess the effectiveness of emission mitigation efforts. We modeled ambient PM2.5 concentrations from 1980 to 2014 based on three conditional scenarios: realistic conditions, fixed emissions, and fixed meteorology. The differences found between the model outputs were analyzed to quantify the relative contributions of emissions and meteorological factors. Emission-related gridded PM2.5 concentrations excluding the meteorological effects were predicted using multivariate regression models, whereas meteorological confounding effects on PM2.5 fluctuations were characterized by probabilistic functions. When the regression models and probabilistic functions were combined, fluctuations in the PM2.5 concentrations induced by emissions and meteorological factors were quantified for all model grid cells and regions. The method was then applied to assess the historical and future trends of PM2.5 concentrations and potential fluctuations on global, national, and city scales. The proposed method may thus be used to assess the effectiveness of mitigation actions.

Although PM

Air pollution, a threat to air quality and human health, has attracted ever-increasing attention in recent years. In addition to having local influence, air pollutants can also travel the globe via atmospheric circulation and international trade. Black carbon (BC), emitted from incomplete combustion, is a unique but representative particulate pollutant. This study tracked down the BC aerosol and its direct radiative forcing to the emission sources and final consumers using the global chemical transport model (MOZART-4), the rapid radiative transfer model for general circulation simulations (RRTM), and a multiregional input-output analysis (MRIO). BC was physically transported (i.e., atmospheric transport) from western to eastern countries in the midlatitude westerlies, but its magnitude is near an order of magnitude higher if the virtual flow embodied in international trade is considered. The transboundary effects on East and South Asia by other regions increased from about 3% (physical transport only) to 10% when considering both physical and virtual transport. The influence efficiency on East Asia was also large because of the comparatively large emission intensity and emission-intensive exports (e.g., machinery and equipment). The radiative forcing in Africa imposed by consumption from Europe, North America, and East Asia (0.01 Wm-2) was even larger than the total forcing in North America. Understanding the supply chain and incorporating both atmospheric and virtual transport may improve multilateral cooperation on air pollutant mitigation both domestically and internationally.

Emission quantification of primary particulate matter (PM) is essential for assessment of its related climate and health impacts. To reduce uncertainty associated with global emissions of PM2.5, PM10, and TSP, we compiled data with high spatial (0.1° × 0.1°) and sectorial (77 primary sources) resolutions for 2007 based on a newly released global fuel data product (PKU-FUEL-2007) and an emission factor database. Our estimates for developing countries are higher than those previously reported. Spatial bias associated with large countries could be reduced by using subnational fuel consumption data. Additionally, we looked at temporal trends from 1960 to 2009 at country-scale resolution. Although total emissions are still increasing in developing countries, their intensities in terms of gross domestic production or energy consumption have decreased. PM emitted in developed countries is finer owing to a larger contribution from nonindustrial sources and use of abatement technologies. In contrast, countries like China, with strong industry emissions and limited abatement facilities, emit coarser PM. The health impacts of PM are intensified in hotspots and cities owing to covariance of sources and receptors. Although urbanization reduces the per person emission, overall health impacts related to these emissions are heightened because of aggregation effects.

The quantification of nitrogen oxide (NOx) emissions is critical for air quality modeling. Based on updated fuel consumption and emission factor databases, a global emission inventory was compiled with high spatial (0.1° × 0.1°), temporal (monthly), and source (87 sources) resolutions for the period 1960 to 2014. The monthly emission data have been uploaded online (http://inventory.pku.edu.cn), along with a number of other air pollutant and greenhouse gas data for free download. Differences in source profiles, not global total quantities, between our results and those reported previously were found. There were significant differences in total and per capita emissions and emission intensities among countries, especially between the developing and developed countries. Globally, the total annual NOx emissions finally stopped increasing in 2013 after continuously increasing over several decades, largely due to strict control measures taken in China in recent years. Nevertheless, the peak year of NOx emissions...

Population migration has upgraded the direct energy consumption with remarkable benefits on air quality and health in China., Direct residential and transportation energy consumption (RTC) contributes significantly to ambient fine particulate matter with a diameter smaller than 2.5 μm (PM2.5) in China. During massive rural-urban migration, population and pollutant emissions from RTC have evolved in terms of magnitude and geographic distribution, which was thought to worsen PM2.5 levels in cities but has not been quantitatively addressed. We quantify the temporal trends and spatial patterns of migration to cities and evaluate their associated pollutant emissions from RTC and subsequent health impact from 1980 to 2030. We show that, despite increased urban RTC emissions due to migration, the net effect of migration in China has been a reduction of PM2.5 exposure, primarily because of an unequal distribution of RTC energy mixes between urban and rural areas. After migration, people have switched to cleaner fuel types, which considerably lessened regional emissions. Consequently, the national average PM2.5 exposure concentration in 2010 was reduced by 3.9 μg/m3 (90% confidence interval, 3.0 to 5.4 μg/m3) due to migration, corresponding to an annual reduction of 36,000 (19,000 to 47,000) premature deaths. This reduction was the result of an increase in deaths by 142,000 (78,000 to 181,000) due to migrants swarming into cities and decreases in deaths by 148,000 (76,000 to 194,000) and 29,000 (15,000 to 39,000) due to transitions to a cleaner energy mix and lower urban population densities, respectively. Locally, however, megacities such as Beijing and Shanghai experienced increases in PM2.5 exposure associated with migration because these cities received massive immigration, which has driven a large increase in local emissions.