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Intensive agricultural activities in the North China Plain (NCP) lead to substantial emissions of nitrogen oxides (NOx) from soil, while the role of this source on local severe ozone pollution is unknown. Here we use a mechanistic parameterization of soil NOx emissions combined with two atmospheric chemistry models to investigate the issue. We find that the presence of soil NOx emissions in the NCP significantly reduces the sensitivity of ozone to anthropogenic emissions. The maximum ozone air quality improvements in July 2017, as can be achieved by controlling all domestic anthropogenic emissions of air pollutants, decrease by 30% due to the presence of soil NOx. This effect causes an emission control penalty such that large additional emission reductions are required to achieve ozone regulation targets. As NOx emissions from fuel combustion are being controlled, the soil emission penalty would become increasingly prominent and shall be considered in emission control strategies. Summertime ozone air pollution in North China remains severe. Here the authors find large biogenic emissions of nitrogen oxides in North China, mainly driven by fertilizer application, challenge the mitigation of ozone pollution by only reducing combustion induced ozone precursors' emissions. [ABSTRACT FROM AUTHOR]

We present a new product with explicit aerosol corrections, POMINO-TROPOMI, for tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs) over East Asia, based on the newly launched TROPOspheric Monitoring Instrument with an unprecedented high horizontal resolution. Compared to the official TM5-MP-DOMINO (OFFLINE) product, POMINO-TROPOMI shows stronger concentration gradients near emission source locations and better agrees with MAX-DOAS measurements (R2=0.75 ; NMB=0.8 % versus R2=0.68 , NMB=-41.9 %). Sensitivity tests suggest that implicit aerosol corrections, as in TM5-MP-DOMINO, lead to underestimations of NO2 columns by about 25 % over the polluted northern East China region. Reducing the horizontal resolution of a priori NO2 profiles would underestimate the retrieved NO2 columns over isolated city clusters in western China by 35 % but with overestimates of more than 50 % over many offshore coastal areas. The effect of a priori NO2 profiles is more important under calm conditions. [ABSTRACT FROM AUTHOR]

Emission datasets of nitrogen oxides (NOx) at high horizontal resolutions (e.g., 0.05∘×0.05∘) are crucial for understanding human influences at fine scales, air quality studies, and pollution control. Yet high-resolution emission data are often missing or contain large uncertainties especially for the developing regions. Taking advantage of long-term satellite measurements of nitrogen dioxide (NO2), here we develop a computationally efficient method of estimating NOx emissions in major urban areas at the 0.05∘×0.05∘ resolution. The top-down inversion method accounts for the nonlinear effects of horizontal transport, chemical loss, and deposition. We construct a two-dimensional Peking University High-resolution Lifetime-Emission-Transport model (PHLET), its adjoint model (PHLET-A), and a satellite conversion matrix approach to relate emissions, lifetimes, simulated NO2 , and satellite NO2 data. The inversion method is applied to the summer months of 2012–2015 in the Yangtze River Delta (YRD; 29–34 ∘ N, 118–123 ∘ E) area, a major polluted region of China, using the NO2 vertical column density data from the Peking University Ozone Monitoring Instrument NO2 product (POMINO). A systematic analysis of inversion errors is performed, including using an independent test based on GEOS-Chem simulations. Across the YRD area, the summer average emissions obtained in this work range from 0 to 15.3 kg km -2 h -1 , and the lifetimes (due to chemical loss and deposition) range from 0.6 to 3.3 h. Our emission dataset reveals fine-scale spatial information related to nighttime light, population density, road network, maritime shipping, and land use (from a Google Earth photo). We further compare our emissions with multiple inventories. Many of the fine-scale emission structures are not well represented or not included in the widely used Multi-scale Emissions Inventory of China (MEIC). [ABSTRACT FROM AUTHOR]

Seven scenarios were designed to study the national environmental benefits of ULE in coal-fired power plants (CPPs), ULE in industrial coal burning (ICB) and NH3 emission reduction by using the GEOS-Chem model. The results showed that although the CPPs have achieved the ULE transformation target, the PM2.5 concentration across the country has decreased by 4.8% (1.4 μg/m3). Due to the complex non-linear chemical competition mechanism among nitrate and sulfate, the average concentration of nitrate in the country has increased by 1.5% (0.1 μg/m3), which has reduced the environmental benefits of the power plant emission reduction. If the ULE technology is applied to the ICB to further reduce NOx and SO2, although the PM2.5 concentration can be reduced by 10.1% (2.9 μg/m3), the concentration of nitrate will increase by 2.7% (0.2 μg/m3). Based on the CPPs-ULE, NH3 emissions reduced by 30% and 50% can significantly reduce the concentration of ammonium and nitrate, so that the PM2.5 concentration is decreased by 11.5% (3.3 μg/m3) and 16.5% (4.7 μg/m3). Similarly, based on the CPPs-ICB-ULE, NH3 emissions can be reduced by 30% and 50% and the PM2.5 concentration reduced by 15.6% (4.4 μg/m3) and 20.3% (5.8 μg/m3). The CPPs and ICB use the ULE technology to reduce NOx and SO2, thereby reducing the concentration of ammonium and sulfate, causing the PM2.5 concentration to decline, and NH3 reduction is mainly achieved through reducing the concentration of ammonium and nitrate to reduce the concentration of PM2.5. In order to better reduce the concentration of PM2.5, NOx, SO2 and NH3 emission reduction control measures should be comprehensively considered in different regions of China. By comprehensively considering the economic cost and environmental benefits of ULE in ICB and NH3 emission reduction, an optimal haze control scheme can be determined. [ABSTRACT FROM AUTHOR]

In order to combat environmental pollution, China enacted the Environmental Protection Tax Law in early 2018. Yet the impacts of the environmental tax on individual regions with different socioeconomic statuses, which are crucial for social justice and public acceptance, remain unclear. Based on a Multi-Regional Input-Output (MRIO) table and a nationally regulated tax payment calculation method, this study analyzes the distributional impacts of an environmental tax based upon province's consumption from both inter-provincial and rural-urban aspects. The national tax revenue based on the current levy mechanism is estimated to be only one seventh of the economic loss from premature mortality caused by ambient particulate matter (PM 2.5). The taxation may slightly alleviate urban-rural inequality but may not be helpful with reducing inter-provincial inequality. We further analyze two alternative levy mechanisms. If each province imposes taxes to products it consumes (rather than produces, as in the current mechanism), with the tax rate linearly dependent on its per capita consumption expenditure, this would moderately increase the national tax revenue and significantly reduce inter-provincial inequality. To better compensate for the economic costs of air pollution and reduce regional inequality, it would be beneficial to increase the tax rate nationwide and implement a levy mechanism based on provincially differentiated levels of consumption and economic status. [ABSTRACT FROM AUTHOR]

Despite much effort made in studying human health associated with fine particulate matter (PM 2.5), our knowledge about PM 2.5 and human health from a long-term perspective is still limited by inadequately long data. Here, we presented a novel method to retrieve surface PM 2.5 mass concentrations using surface visibility measurements and GEOS-Chem model simulations. First, we used visibility measurements and the ratio of PM 2.5 and aerosol extinction coefficient (AEC) in GEOS-Chem to calculate visibility-inferred PM 2.5 at individual stations (SC-PM 2.5). Then we merged SC-PM 2.5 with the spatial pattern of GEOS-Chem modeled PM 2.5 to obtain a gridded PM 2.5 dataset (GC-PM 2.5). We validated the GC-PM 2.5 data over the North China Plain on a 0.3125° longitude x 0.25° latitude grid in January, April, July and October 2014, using ground-based PM 2.5 measurements. The spatial patterns of temporally averaged PM 2.5 mass concentrations are consistent between GC-PM 2.5 and measured data with a correlation coefficient of 0.79 and a linear regression slope of 0.8. The spatial average GC-PM 2.5 data reproduce the day-to-day variation of observed PM 2.5 concentrations with a correlation coefficient of 0.96 and a slope of 1.0. The mean bias is less than 12 μg/m3 (<14%). Future research will validate the proposed method using multi-year data, for purpose of studying long-term PM 2.5 variations and their health impacts since 1980. • We integrate visibility data and GEOS-Chem simulations to estimate PM 2.5 concentrations in 2014 over North China. • Visibility converted PM 2.5 are spatiotemporally consistent with PM 2.5 measurements. • Our method provides a novel, plausible way to retrieve long-term variation of PM 2.5. [ABSTRACT FROM AUTHOR]

Emission datasets of nitrogen oxides (NOx) at high horizontal resolutions (e.g., 0.05°×0.05°) are crucial for understanding human influences at fine scales, air quality studies, and pollution control. Yet high-resolution emission data are often lacking or contain large uncertainties especially for the developing regions. Taking advantage of long-term satellite measurements of nitrogen dioxide (NO2), here we develop a computationally efficient method to inverting NOx emissions in major urban areas at the 0.05°×0.05° resolution. The inversion accounts for the nonlinear effects of horizontal transport, chemical loss, and deposition. We construct a 2-dimensional Peking University High-resolution Lifetime-Emission-Transport (PHLET) model, its adjoint model (PHLET-A), and a Satellite Conversion Metrix approach to relate emissions, simulated NO2, and satellite NO2 data. The inversion method is applied to summer months of 2012–2016 in the Yangtze River Delta area (YRD, 118°E-123°E, 29°N-34°N), a major polluted region of China, using the POMINO NO2 vertical column density product retrieved from the Ozone Monitoring Instrument. A systematic analysis of inversion errors is performed, including using an Observing System Simulation Experiment-like test. Across the YRD area, the inverted summer average emission ranges from 0 to 12.0 kg km-2 h-1, and the lifetime (due to chemical loss and deposition) from 1.4 to 3.6 h. Our inverted emission dataset reveals fine-scale spatial information tied to nighttime light, population density, road network, and maritime shipping. Many of the inverted fine-scale emission features are not well represented or not included in the widely used Multi-scale Emissions Inventory of China. Our inversion method can be applied to other regions and other satellite sensors such as the TROPOspheric Monitoring Instrument. [ABSTRACT FROM AUTHOR]

High-resolution (e.g., 5 km) emission data of nitrogen oxides (NO x = NO + NO 2 ) provide localized knowledge of pollution sources for targeted regulations, yet such data are lacking or inaccurate over most regions at present. Here we improve our PHLET-based inversion method to derive NO x emissions in China at a 5-km resolution in summer 2019, based on the TROPOMI-POMINO satellite product of nitrogen dioxide (NO 2 ) columns. With low computational costs, our inversion explicitly accounts for the effects of horizontal transport and nonlinear chemistry. We find numerous small-to-medium sources related to minor roads and small human settlements at relatively low affluence levels, in addition to clear emission signals along major transportation lines, consistent with road line density and Tencent location data. Many small-to-medium sources and transportation emissions are unclear or missing in the spatial distributions of four widely used emission inventories. Our emissions offer a unique reference for targeted emission control.

In a globalized economy, production of goods can be disrupted by trade disputes. Yet the resulting impacts on carbon dioxide emissions and ambient particulate matter (PM 2.5 ) related premature mortality are unclear. Here we show that in contrast to a free trade world, with the emission intensity in each sector unchanged, an extremely anti-trade scenario with current tariffs plus an additional 25% tariff on each traded product would reduce the global export volume by 32.5%, gross domestic product by 9.0%, carbon dioxide by 6.3%, and PM 2.5 -related mortality by 4.1%. The respective impacts would be substantial for the United States, Western Europe and China. A freer trade scenario would increase global carbon dioxide emission and air pollution due to higher levels of production, especially in developing regions with relatively high emission intensities. Global collaborative actions to reduce emission intensities in developing regions could help achieve an economic-environmental win-win state through globalization.