China has enacted a number of ambitious pollution control policies to mitigate air pollution in urban areas. Unintended side effects of these policies to other environmental policy arenas and regions have largely been ignored. To bridge this gap, we use a multiregional input-output model in combination with an atmospheric chemical transport model to simulate clean air policy scenarios and evaluate their environmental impacts on primary PM_2.5 and secondary precursor emissions, as well as CO₂ emissions and water consumption, in the target region and spillover effects to other regions. Our results show that the reduction in primary PM_2.5 and secondary precursor emissions in the target regions comes at the cost of increasing emissions especially in neighboring provinces. Similarly, co-benefits of lower CO₂ emissions and reduced water consumption in the target region are achieved at the expense of higher impacts elsewhere, through outsourcing production to less developed regions in China.

Recent studies have shown that surface ozone (O ₃ ) concentrations over central eastern China (CEC) have increased significantly during the past decade. We quantified the effects of changes in meteorological conditions and O ₃ precursor emissions on surface O ₃ levels over CEC between July 2003 and July 2015 using the GEOS-Chem model. The simulated monthly mean maximum daily 8 h average O ₃ concentration (MDA8 O ₃ ) in July increased by approximately 13.6 %, from 65.5±7.9 ppbv (2003) to 74.4±8.7 ppbv (2015), comparable to the observed results. The change in meteorology led to an increase in MDA8 O ₃ of 5.8±3.9 ppbv over the central part of CEC, in contrast to a decrease of about -0.8±3.5 ppbv over the eastern part of the region. In comparison, the MDA8 O ₃ over the central and eastern parts of CEC increased by 3.5±1.4 and 5.6±1.8 ppbv due to the increased emissions. The increase in averaged O ₃ in the CEC region resulting from the emission increase (4.0±1.9 ppbv) was higher than that caused by meteorological changes (3.1±4.9 ppbv) relative to the 2003 standard simulation, while the regions with larger O ₃ increases showed a higher sensitivity to meteorological conditions than to emission changes. Sensitivity tests indicate that increased levels of anthropogenic non-methane volatile organic compounds (NMVOCs) dominate the O ₃ increase over the eastern part of CEC, and anthropogenic nitrogen oxides (NO _x ) mainly increase MDA8 O ₃ over the central and western parts and decrease O ₃ in a few urban areas in the eastern part. Budget analysis showed that net photochemical production and meteorological conditions (transport in particular) are two important factors that influence O ₃ levels over the CEC. The results of this study suggest a need to further assess the effectiveness of control strategies for O ₃ pollution in the context of regional meteorology and anthropogenic emission changes.

Satellite retrieval of vertical column densities (VCDs) of tropospheric nitrogen dioxide (NO₂) is critical for NO_x pollution and impact evaluation. For regions with high aerosol loadings, the retrieval accuracy is greatly affected by whether aerosol optical effects are treated implicitly (as additional “effective” clouds) or explicitly, among other factors. Our previous POMINO algorithm explicitly accounts for aerosol effects to improve the retrieval, especially in polluted situations over China, by using aerosol information from GEOS-Chem simulations with further monthly constraints by MODIS/Aqua aerosol optical depth (AOD) data. Here we present a major algorithm update, POMINO v1.1, by constructing a monthly climatological dataset of aerosol extinction profiles, based on level 2 CALIOP/CALIPSO data over 2007–2015, to better constrain the modeled aerosol vertical profiles. We find that GEOS-Chem captures the month-to-month variation in CALIOP aerosol layer height (ALH) but with a systematic underestimate by about 300–600 m (season and location dependent), due to a too strong negative vertical gradient of extinction above 1 km. Correcting the model aerosol extinction profiles results in small changes in retrieved cloud fraction, increases in cloud-top pressure (within 2 %–6 % in most cases), and increases in tropospheric NO₂ VCD by 4 %–16 % over China on a monthly basis in 2012. The improved NO₂ VCDs (in POMINO v1.1) are more consistent with independent ground-based MAX-DOAS observations (R²=0.80, NMB = −3.4 %, for 162 pixels in 49 days) than POMINO (R²=0.80, NMB = −9.6 %), DOMINO v2 (R²=0.68, NMB = −2.1 %), and QA4ECV (R²=0.75, NMB = −22.0 %) are. Especially on haze days, R² reaches 0.76 for POMINO v1.1, much higher than that for POMINO (0.68), DOMINO v2 (0.38), and QA4ECV (0.34). Furthermore, the increase in cloud pressure likely reveals a more realistic vertical relationship between cloud and aerosol layers, with aerosols situated above the clouds in certain months instead of always below the clouds. The POMINO v1.1 algorithm is a core step towards our next public release of the data product (POMINO v2), and it will also be applied to the recently launched S5P-TROPOMI sensor.

Eastern China (27–41° N, 110–123° E) is heavily polluted by nitrogen dioxide (NO₂), particulate matter with aerodynamic diameter below 2.5 µm (PM_2.5), and other air pollutants. These pollutants vary on a variety of temporal and spatial scales, with many temporal scales that are nonperiodic and nonstationary, challenging proper quantitative characterization and visualization. This study uses a newly compiled EOF–EEMD analysis visualization package to evaluate the spatiotemporal variability of ground-level NO₂, PM_2.5, and their associations with meteorological processes over Eastern China in fall–winter 2013. Applying the package to observed hourly pollutant data reveals a primary spatial pattern representing Eastern China synchronous variation in time, which is dominated by diurnal variability with a much weaker day-to-day signal. A secondary spatial mode, representing north–south opposing changes in time with no constant period, is characterized by wind-related dilution or a buildup of pollutants from one day to another. We further evaluate simulations of nested GEOS-Chem v9-02 and WRF/CMAQ v5.0.1 in capturing the spatiotemporal variability of pollutants. GEOS-Chem underestimates NO₂ by about 17 µg m⁻³ and PM_2.5 by 35 µg m⁻³ on average over fall–winter 2013. It reproduces the diurnal variability for both pollutants. For the day-to-day variation, GEOS-Chem reproduces the observed north–south contrasting mode for both pollutants but not the Eastern China synchronous mode (especially for NO₂). The model errors are due to a first model layer too thick (about 130 m) to capture the near-surface vertical gradient, deficiencies in the nighttime nitrogen chemistry in the first layer, and missing secondary organic aerosols and anthropogenic dust. CMAQ overestimates the diurnal cycle of pollutants due to too-weak boundary layer mixing, especially in the nighttime, and overestimates NO₂ by about 30 µg m⁻³ and PM_2.5 by 60 µg m⁻³. For the day-to-day variability, CMAQ reproduces the observed Eastern China synchronous mode but not the north–south opposing mode of NO₂. Both models capture the day-to-day variability of PM_2.5 better than that of NO₂. These results shed light on model improvement. The EOF–EEMD package is freely available for noncommercial uses.

Emission datasets of nitrogen oxides (NO_x) at high horizontal resolutions (e.g., $\mathrm{0.05}{}^{\circ }\times \mathrm{0.05}{}^{\circ }$ ) 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 NO_x emissions in major urban areas at the $\mathrm{0.05}{}^{\circ }\times \mathrm{0.05}{}^{\circ }$ 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).