While great efforts have been made to China’s clean air actions since 2013 and effectively mitigated PM2.5 pollution, the emission-concentration relationships and cost-effectiveness may have changed substantially and are poorly constrained. Large emission reductions during the COVID-19 lockdown period in early 2020 did not similarly alleviate PM2.5 pollution in North China, reflecting a distinct nonlinear chemical response of PM2.5 formation to emission changes. At the same time, strengthened emissions standards and elimination of outdated industrial capacities have replaced the existing technologies and increased the cost of pollution control. Here we apply emission-concentration relationships for PM2.5 diagnosed using the adjoint approach to quantitatively assess how chemical nonlinearity affects PM2.5 over Beijing in February 2020 in response to two emission reduction scenarios: the COVID-19 lockdown and 2013-2017 emission controls, and further evaluate the marginal cost and benefit of possible technological alternatives. We find that, in the absence of chemical nonlinearity, the COVID-19 lockdown would decrease PM2.5 in Beijing by 10.6 μg m-3, and the 2013-2017 emission controls resulted in a larger decrease of 54.2 μg m-3 because of greater reductions of SO2 and primary aerosol emissions. Chemical nonlinearity offset the decrease for Beijing PM2.5 by 4.7 μg m-3 in lockdown, which was mainly attributed to enhanced sensitivity of aerosol nitrate to NOx emissions, but enhanced the efficiency of 2013-2017 emission controls by 12.5 μg m-3 due to the weakened heterogeneous reaction of sulfate. For further PM2.5 mitigation, emission reductions in ammonia by urea substitution and primary PM2.5 with electrostatic precipitator have high PM2.5 reduction potential and cost effectiveness. Such chemical nonlinearity and cost optimization are important to estimate and consider when designing or assessing air pollution control strategies.

Ammonia emissions in China mainly came from agricultural activities. Excess emissions could lead to degraded air quality and excess nitrogen deposition. Therefore, it is essential to improve air quality and nitrogen deposition through agricultural ammonia reduction measures. On the basis of the existing research, this study established an Agricultural Management Technology-Ammonia emission assessment platform with 51 measures of fertilizer application and 53 measures of livestock farming derived from a literature review and adopted the Monte Carlo method to apply this platform to Beijing-Tianjing-Heibei (BTH) region where active agricultural activities occur. An updated agricultural ammonia emission inventory at 3-km resolution in BTH region was used in this study. We find that ammonia emissions from livestock farming could be reduced by 79-151Gg (30%-57%) and from fertilizer application by 58-163Gg (18%-51%) in BTH region in 2019. We applied two reduction scenarios that could achieve average and maximum ammonia emission reduction based on the Monte Carlo results, and evaluated the resulting improvements of air quality and deposition using the GCHP model with a resolution of 10km × 10km in BTH region.The results show that the baseline of PM2.5 concentration, NHX and NOy deposition in BTH region in 2019 is 27-61 µg/m3, 8-57 Gg N/month and 3-51 Gg N/month. Under two ammonia emission reduction scenarios, PM2.5 concentration and NHx deposition would, respectively, reduce 1.38-3.89 µg/m3, 3-14 Gg N/month while NOy deposition would increase 0.5-2 Gg N/month. Our research shows that agricultural ammonia has great emission reduction potential that would benefit to the reduction of nitrogen pollution.

Air quality in China has been significantly improved over the past decade. However, many areas still face challenges with air pollutants such as PM2.5 and ozone. More than a quarter of the 339 cities regularly exceed the Chinese air quality standards and all of them exceed the latest World Health Organization guidelines, despite compliance efforts in reducing sulfur dioxide (SO2), nitrogen oxides (NOx), and carbon emissions. Continuous and deep improvements in air quality need a more in-depth quantitative assessment to understand the spatial scales (urban, regional, and national) of air pollution sources and to clarify the sequence of precursors and emission sectors that contribute to these pollutants. Here we combine the WRF-Chem model sensitivity simulations and the Screening for High Emission Reduction Potentials for Air quality (SHERPA) tool to address these challenges, particularly focusing on PM2.5. We first assess the chemical regimes of secondary inorganic aerosols (SIA) formation by simulating emission reduction scenarios for three main precursors (SO2, NOx, NH3). We find that NH3 predominantly controls SIA formation over 60% of China during cold seasons and NOx- or SO2-senesitive grids only dominate in four warm months (April to July). The spatial distributions of the chemical regimes throughout the year show a distinct demarcation between eastern NH3-NOx-controlled area and western NH3-SO2(-NOx)-controlled area. Sichuan Basin and Henan Province, however, are NOx-sensitive throughout the year. We then train the source-receptor relationships in SHERPA using the baseline and sensitivity simulation outputs of WRF-Chem. SHERPA can well reproduce the responses of PM2.5 concentrations to the emission changes of all precursors in China. Our results show that for yearly average PM2.5, local actions of precursor emission reduction at the urban scale are effective for most cities and mitigations in agricultural sector are important. Our study stresses the essential role of NH3 to further PM2.5 mitigation strategies of whole China.

Accurate modelling of tropospheric ozone is crucial for understanding its climate and health effect, yet the uncertainty associated with natural ozone precursor emissions such as lightning and soil NOx is often overlooked. Here we apply a global chemical transport model, GEOS-Chem High Performance, to explore this uncertainty. The modelled present-day tropospheric ozone burden, under low to high natural NOx emissions levels (set to align with the current literature’s range), varies from 285 to 373 Tg; primarily attributed to lightning NOx uncertainty. Such a range far exceeds the ozone difference driven by anthropogenic emissions between the two most disparate SSP scenarios in 2050 (33 Tg). Ozone’s sensitivity to natural emissions is the highest around the tropical upper troposphere where ozone’s climate effect is also large, and would be even higher if anthropogenic emissions were reduced along the SSP1-2.6 pathway. At the surface, global mean warm-season ozone ranges from 32.4 to 38.8 ppbv, mainly due to soil NOx. This especially introduces large ozone uncertainties in southern hemisphere regions such as the Amazon and Australia. We also examine ΔO3-anthro, the ozone change driven by anthropogenic emissions changes up-to 2050. We found that with respect to tropospheric ozone burden, ΔO3-anthro shows limited differences between high and low natural emission levels (~13%), implying that the estimate of future changes in ozone radiative forcing is subject to less uncertainty from uncertain natural emissions than the present-day ozone radiative forcing itself. However, ΔO3-anthro related to the surface ozone exposure metric shows significant contrasts with different natural NOx emissions. The largest difference exceeds 5 ppbv (~50%) in regions such as Europe, North America, eastern China, and India. We hence stress that extra care needs to be taken when using individual models to assess ozone health risks in these densely populated regions as highly uncertain natural emissions will produce a presently unconstrained error.

High ammonia (NH3) emissions mostly from agricultural sources have contributed to PM2.5 air pollution and excess nitrogen deposition harmful to human and ecosystem health in China. Here we develop an assessment framework combining an agricultural management technology database and evaluate technology combinations for their potentials in NH3 emission reductions and consequent air quality and ecosystem benefits for the Beijing-Tianjin-Hebei (BTH) region. Results show that BTH agricultural NH3 emissions can be reduced by up to 57% (274 Gg of N per year) in 2019. With maximum feasible NH3 reduction (57%), annual PM2.5 concentrations and nitrogen deposition in BTH can be reduced by up to 7% and 13%, respectively, which are more significant than the effects of halving local anthropogenic NOx emissions. When combining NH3 and NOx emission reductions, the effects of NH3 controls on PM2.5 mitigation will be suppressed while facilitating more efficient local nitrogen deposition mitigation. Our findings implicate that technically feasible NH3 emission reductions are still useful for current PM2.5 management and nitrogen deposition mitigation. In the near future, with continuous NOx controls, additional NH3 controls are required to further mitigate nitrogen deposition in BTH, while associated air quality benefits would depend nonlinearly upon the levels of NH3 and NOx emission reductions.

As greenhouse gases and air pollutants are often co-emitted, the co-benefits on air quality improvement arising from implementing low-carbon policies are drawing much attention. Here we focus our research on the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in south-eastern China, the demonstration zone for air pollution prevention and control initiatives of China, while still contending with severe ozone pollution, particularly in autumn. This study assesses whether China’s ‘Dual-Carbon’ targets contribute to the GBA achieving the national ozone control goals and offers corresponding mitigation strategies. We developed an external module to softly couple an integrated assessment model IMEDCGE (Integrated Model of Energy, Environment, and Economy for Sustainable Development, Computable General Equilibrium) with the WRF-Chem atmospheric chemistry transport model. The fusion allows us to project the regional ozone pollution evolutions from the base year (2015) to 2050 under various future scenarios. We explore three anthropogenic emission reduction pathways, each reflecting different levels in climate change mitigation targets and end-of-pipe control policies, resulting in varied ozone precursor reduction patterns. The results show that implementing China’s ‘Dual-Carbon’ policies, combined with stringent end-of-pipe control measures, will substantially decrease the averaged MDA8 surface ozone across the GBA to below 90 μg m-3 by 2050. However, different ozone concentration trends emerge between the southern and northern regions due to spatial variations in ozone chemical regimes, as indicated by H2O2/HNO3 ratios. Overall, NOx emission reduction will become increasingly effective in curbing ozone pollution till 2050, while NMVOCs emission reduction, driven by strict end-of-pipe control policies, plays a pivotal role in the short term (before 2030). Even under the most ideal scenario, where more than 90% of local anthropogenic NOx and 85% of NMVOCs emissions are eliminated, our findings underscore the imperative of coordinated efforts across all sectors and collaborative emission reduction beyond the GBA to mitigate ozone pollution effectively.

Surface background ozone, defined as the ozone in the absence of domestic anthropogenic emissions, is important for developing emission reduction strategies. Here we apply the recently developed GEOS-Chem High Performance (GCHP) global atmospheric chemistry model with ∼0.5° stretched resolution over China to understand the sources of Chinese background ozone (CNB) in the metric of daily maximum 8 h average (MDA8) and to identify the drivers of its interannual variability (IAV) from 2015 to 2019. The GCHP ozone simulations over China are evaluated with an ensemble of surface and aircraft measurements. The five-year national-mean CNB ozone is estimated as 37.9 ppbv, with a spatially west-to-southeast downward gradient (55 to 25 ppbv) and a summer peak (42.5 ppbv). High background levels in western China are due to abundant transport from the free troposphere and adjacent foreign regions, while in eastern China, domestic formation from surface natural precursors is also important. We find greater importance of soil nitric oxides (NOx) than biogenic volatile organic compound emissions to CNB ozone in summer (6.4 vs. 3.9 ppbv), as ozone formation becomes increasingly NOx-sensitive when suppressing anthropogenic emissions. The percentage of daily CNB ozone to total surface ozone generally decreases with increasing daily total ozone, indicating an increased contribution of domestic anthropogenic emissions on polluted days. CNB ozone shows the largest IAV in summer, with standard deviations (seasonal means) of ∼5 ppbv over Qinghai-Tibet Plateau (QTP) and >3.5 ppbv in eastern China. CNB values in QTP are strongly correlated with horizontal circulation anomalies in the middle troposphere, while soil NOx emissions largely drive the IAV in the east. El Nino can inhibit CNB ozone formation in Southeast China by increased precipitation and lower temperature locally in spring, but enhance CNB in Southwest China through increased biomass burning emissions in Southeast Asia.

Many studies have been conducted to address the surface ozone pollution issue over China, but few model works focus on the background ozone, a metric to represent the best surface ozone levels that can be achieved through emission controls. Here we apply a state-of-art global chemical transport model GEOS-Chem High Performance (GCHP) to understand the sources contributing to Chinese background (CNB) daily maximum 8 h average (MDA8) ozone, and to identify the driving factor of its interannual variability from 2015 to 2019.The five-year-mean CNB ozone is estimated as 37.8 ppbv, showing a general west-to-east downward gradient. The national-mean CNB ozone levels are the largest in summer (42.5 ppbv), but distinct seasonality can be seen at different regions. Using the tagged ozone technique, we show that the high background levels in western China are due to abundant transport from the free troposphere and adjacent foreign regions, while in eastern China, domestic ozone formation near surface from natural precursors is also important and exhibits intensive seasonal variation. CNB ozone enhancements from lightning NOx, soil NOx, and biogenic volatile organic compound (BVOC) emissions are estimated as 8.1, 6.4 and 3.9 ppbv, respectively, in summertime. We found the greater importance of BVOC over soil NOx to ozone as reported in previous studies is reversed when domestic anthropogenic emissions are turned off, reflecting a more NOx-sensitive ozone chemical regime in a “clean” atmosphere.The interannual variability (IAV) of CNB ozone shows the peak in summer, with standard deviation values during five years of ~5 ppbv over Qinghai-Tibet Plateau (QTP) and >3.5 ppbv over vast eastern China. CNB levels in QTP are found to be well correlated with horizontal circulation anomalies at 500 hPa, while in the east, year-to-year changes in soil NOx emissions dominate the IAV of CNB ozone. We further explore the role of El Nino-Southern Oscillation (ENSO) in modulating the IAV of CNB ozone over southern China in spring. Compared to the La Nina event, the enhanced precipitation and decreased temperature during El Nino inhibit CNB ozone formation in southeast China. However, heat and drought events, as well as enhanced biomass burning emissions during El Nino in Mainland Southeast Asia can largely worsen ozone there, and further rise CNB levels in southwest China under the southerly wind.

Machine learning methods are increasingly used in air quality studies to predict air pollution levels, while few applied them to diagnose and improve the underlying mechanisms controlling air pollution represented in chemical transport models (CTMs). Here, we use the random forest (RF) method to diagnose high biases of surface daily maximum 8 h average (MDA8) ozone concentrations in the GEOS-Chem CTM evaluated against measurements from the nationwide monitoring network in summer 2018 over China. The feature importance results show that cloud optical depth (COD), relative humidity, and precipitation are the top three factors affecting CTM high biases. Such results indicate that the high ozone biases in summer over China mainly occur on wet/cloudy days (∼40% biased high), while biases on dry/clear days are small (within 5%). We link the important features with model parameterizations and variables, identifying model underestimates in the dry deposition velocity and COD on wet/cloudy days. By accounting for the enhanced dry deposition on wet plant cuticles and using satellite observation constrained COD, we find that CTM high ozone biases can be halved with an improved agreement in the temporal variability, highlighting the effects of dry deposition and COD on ozone, as suggested by the RF outcomes.

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.

Urban air pollution has tremendous spatial variability at scales ranging from kilometers to meters due to unevenly distributed emission sources, complex flow patterns, and photochemical reactions. However, high-resolution air quality information is not available through traditional approaches such as ground-based measurements and regional air quality models (with typical resolution > 1 km). Here we develop a 10 m resolution air quality model for traffic-related CO pollution based on the Parallelized Large-Eddy Simulation Model (PALM). The model performance is evaluated with measurements obtained from sensors deployed on a taxi platform, which collects data with a comparable spatial resolution to our model. The very high resolution of the model reveals a detailed geographical dispersion pattern of air pollution in and out of the road network. The model results (0.92 ± 0.40 mg m−3) agree well with the measurements (0.90 ± 0.58 mg m−3, n=114 502). The model has similar spatial patterns to those of the measurements, and the r2 value of a linear regression between model and measurement data is 0.50 ± 0.07 during non-rush hours with middle and low wind speeds. A non-linear relationship is found between average modeled concentrations and wind speed with higher concentrations under calm wind speeds. The modeled concentrations are also 20 %–30 % higher in streets that align with the wind direction within ∼ 20∘. We find that streets with higher buildings downwind have lower modeled concentrations at the pedestrian level, and similar effects are found for the variability in building heights (including gaps between buildings). The modeled concentrations also decay fast in the first ∼ 50 m from the nearest highway and arterial road but change slower further away. This study demonstrates the potential of large-eddy simulation in urban air quality modeling, which is a vigorous part of the smart city system and could inform urban planning and air quality management.