Air quality changes during the COVID-19 in YRD region are analyzed. • The WRF-CAMx modelling system is applied to investigate impact of lowered human activities on air quality changes. • Sources of the residual pollution are figured out for policy implications for future air pollution control.

h i g h l i g h t sWe propose a novel hybrid model to forecast PM 2.5 pollution.Using trajectory based geographic parameter as an extra input to ANN model. Applying prediction strategy at different scales and then sum them up. The model is capable to predict the high peaks of PM 2.5 concentrations. a b s t r a c tIn the paper a novel hybrid model combining air mass trajectory analysis and wavelet transformation to improve the artificial neural network (ANN) forecast accuracy of daily average concentrations of PM 2.5 two days in advance is presented. The model was developed from 13 different air pollution monitoring stations in Beijing, Tianjin, and Hebei province (Jing-Jin-Ji area). The air mass trajectory was used to recognize distinct corridors for transport of "dirty" air and "clean" air to selected stations. With each corridor, a triangular station net was constructed based on air mass trajectories and the distances between neighboring sites. Wind speed and direction were also considered as parameters in calculating this trajectory based air pollution indicator value. Moreover, the original time series of PM 2.5 concentration was decomposed by wavelet transformation into a few sub-series with lower variability. The prediction strategy applied to each of them and then summed up the individual prediction results. Daily meteorological forecast variables as well as the respective pollutant predictors were used as input to a multi-layer perceptron (MLP) type of back-propagation neural network. The experimental verification of the proposed model was conducted over a period of more than one year (between September 2013 and October 2014). It is found that the trajectory based geographic model and wavelet transformation can be effective tools to improve the PM 2.5 forecasting accuracy. The root mean squared error (RMSE) of the hybrid model can be reduced, on the average, by up to 40 percent. Particularly, the high PM 2.5 days are almost anticipated by using wavelet decomposition and the detection rate (DR) for a given alert threshold of hybrid model can reach 90% on average. This approach shows the potential to be applied in other countries' air quality forecasting systems.

Ammonia (NH(3)) is one important precursor of inorganic fine particles; however, knowledge of the impacts of NH(3) emissions on aerosol formation in China is very limited. In this study, we have developed China's NH(3) emission inventory for 2005 and applied the Response Surface Modeling (RSM) technique upon a widely used regional air quality model, the Community Multi-Scale Air Quality Model (CMAQ). The purpose was to analyze the impacts of NH(3) emissions on fine particles for January, April, July, and October over east China, especially those most developed regions including the North China Plain (NCP), Yangtze River delta (YRD), and the Pearl River delta (PRD). The results indicate that NH(3) emissions contribute to 8-11% of PM(2.5) concentrations in these three regions, comparable with the contributions of SO(2) (9-11%) and NO(x) (5-11%) emissions. However, NH(3), SO(2), and NO(x) emissions present significant nonlinear impacts; the PM(2.5) responses to their emissions increase when more control efforts are taken mainly because of the transition between NH(3)-rich and NH(3)-poor conditions. Nitrate aerosol (NO(3)(-)) concentration is more sensitive to NO(x) emissions in NCP and YRD because of the abundant NH(3) emissions in the two regions, but it is equally or even more sensitive to NH(3) emissions in the PRD. In high NO(3)(-) pollution areas such as NCP and YRD, NH(3) is sufficiently abundant to neutralize extra nitric acid produced by an additional 25% of NO(x) emissions. The 90% increase of NH(3) emissions during 1990-2005 resulted in about 50-60% increases of NO(3)(-) and SO(4)(2-) aerosol concentrations. If no control measures are taken for NH(3) emissions, NO(3)(-) will be further enhanced in the future. Control of NH(3) emissions in winter, spring, and fall will benefit PM(2.5) reduction for most regions. However, to improve regional air quality and avoid exacerbating the acidity of aerosols, a more effective pathway is to adopt a multipollutant strategy to control NH(3) emissions in parallel with current SO(2) and NO(x) controls in China.

Abstract. Statistical response surface methodology (RSM)is successfully applied for a Community Multi-scale Air Quality model (CMAQ) analysis of ozone sensitivity studies. Prediction performance has been demonstrated through cross validation, out-of-sample validation and isopleth validation. Sample methods and key parameters, including the maximum numbers of variables involved in statistical interpolation and training samples have been tested and selected through computational experiments. Overall impacts from individual source categories which include local/regional NO x and VOC emission sources and NO x emissions from power plants for three megacities -Beijing, Shanghai and Guangzhou -were evaluated using an RSM analysis of a July 2005 modeling study. NO x control appears to be beneficial for ozone reduction in the downwind areas which usually experience high ozone levels, and NO x control is likely to be more effective than anthropogenic VOC control during periods of heavy photochemical pollution. Regional NO x source categories are strong contributors to surface ozone mixing ratios in three megacities. Local NO x emission control without regional involvement may raise the risk of increasing urban ozone levels due to the VOC-limited conditions. However, local NO x control provides considerable reduction of ozone in upper layers (up to 1 km where the ozone chemistry is NO x -limited) and helps improve regional air quality in downwind areas. Stricter NO x emission control has a substantial effect on ozone reduction because of the shift from VOClimited to NO x -limited chemistry. Therefore, NO x emission control should be significantly enhanced to reduce ozone pollution in China.

A number of software tools exist to estimate the health and economic impacts associated with air quality changes. Over the past 15 years, the U.S. Environmental Protection Agency and its partners invested substantial time and resources in developing the Environmental Benefits Mapping and Analysis Program - Community Edition (BenMAP-CE). BenMAP-CE is a publicly available, PC-based open source software program that can be configured to conduct health impact assessments to inform air quality policies anywhere in the world. The developers coded the platform in C# and made the source code available in GitHub, with the goal of building a collaborative relationship with programmers with expertise in other environmental modeling programs. The team recently improved the BenMAP-CE user experience and incorporated new features, while also building a cadre of analysts and BenMAP-CE training instructors in Latin America and Southeast Asia.

• Eight COVID-19 severely affected regions with varied meteorological factors were included. • Multiple regression analysis was used to correlate the weather condition and the spread of COVID-19. • Rt, an indicator to reflect the transmission of infectious diseases, was adopted to analyze the correlation. • Absolute humidity is negatively correlated to the spread of COVID-19 in the selected regions. • Decreasing trend of absolute humidity raises the alarming of the potential COVID-19 spread.

Abstract. The Beijing-Tianjin-Hebei (BTH) region has been suffering from the most severe fine-particle (PM 2.5 ) pollution in China, which causes serious health damage and economic loss. Quantifying the source contributions to PM 2.5 concentrations has been a challenging task because of the complicated nonlinear relationships between PM 2.5 concentrations and emissions of multiple pollutants from multiple spatial regions and economic sectors. In this study, we use the extended response surface modeling (ERSM) technique to investigate the nonlinear response of PM 2.5 concentrations to emissions of multiple pollutants from different regions and sectors over the BTH region, based on over 1000 simulations by a chemical transport model (CTM). The ERSM-predicted PM 2.5 concentrations agree well with independent CTM simulations, with correlation coefficients larger than 0.99 and mean normalized errors less than 1 %. Using the ERSM technique, we find that, among all air pollutants, primary inorganic PM 2.5 makes the largest contribution (24-36 %) to PM 2.5 concentrations. The contribution of primary inorganic PM 2.5 emissions is especially high in heavily polluted winter and is dominated by the industry as well as residential and commercial sectors, which should be prioritized in PM 2.5 control strategies. The total contributions of all precursors (nitrogen oxides, NO x ; sulfur dioxides, SO 2 ; ammonia, NH 3 ; non-methane volatile organic compounds, NMVOCs; intermediate-volatility organic compounds, IVOCs; primary organic aerosol, POA) to PM 2.5 concentrations range between 31 and 48 %. Among these precursors, PM 2.5 concentrations are primarily sensitive to the emissions of NH 3 , NMVOC + IVOC, and POA. The sensitivities increase substantially for NH 3 and NO x and decrease slightly for POA and NMVOC + IVOC with the increase in the emission reduction ratio, which illustrates the nonlinear relationships between precursor emissions and PM 2.5 concentrations. The contributions of primary inorganic PM 2.5 emissions to PM 2.5 concentrations are dominated by local emission sources, which account for over 75 % of the total primary inorganic PM 2.5 contributions. For precursors, however, emissions from other regions could play similar roles to local emission sources in the summer and over the northern part of BTH. The source contribution features for various types of heavy-pollution episodes are distinctly different from each other and from the monthly mean results, illustrating that control strategies should be differentiated based on the major contributing sources during different types of episodes.

Abstract. An innovative extended response surface modeling technique (ERSM v1.0) is developed to characterize the nonlinear response of fine particles (PM2.5) to large and simultaneous changes of multiple precursor emissions from multiple regions and sectors. The ERSM technique is developed based on the conventional response surface modeling (RSM) technique; it first quantifies the relationship between PM2.5 concentrations and the emissions of gaseous precursors from each single region using the conventional RSM technique, and then assesses the effects of inter-regional transport of PM2.5 and its gaseous precursors on PM2.5 concentrations in the target region. We apply this novel technique with a widely used regional chemical transport model (CTM) over the Yangtze River delta (YRD) region of China, and evaluate the response of PM2.5 and its inorganic components to the emissions of 36 pollutant–region–sector combinations. The predicted PM2.5 concentrations agree well with independent CTM simulations; the correlation coefficients are larger than 0.98 and 0.99, and the mean normalized errors (MNEs) are less than 1 and 2% for January and August, respectively. It is also demonstrated that the ERSM technique could reproduce fairly well the response of PM2.5 to continuous changes of precursor emission levels between zero and 150%. Employing this new technique, we identify the major sources contributing to PM2.5 and its inorganic components in the YRD region. The nonlinearity in the response of PM2.5 to emission changes is characterized and the underlying chemical processes are illustrated.