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The Omicron pandemic broke out in Shanghai in March 2022, and some infected people spread to some cities in the Yangtze River Delta (YRD) region. To achieve the dynamic zero-COVID target as soon as possible, Shanghai and nine cities that were heavily affected by Shanghai implemented the lockdown measures. This paper aims to quantify the impact of the lockdown on air quality and human health. A difference-in-difference (DID) model was first used to measure the impact of the lockdown on air quality in these ten cities. Based on the results of the DID model, we estimated the PM2.5-related health and economic benefits using the concentration–response function and the value of statistical life method. Results showed that the lockdown has reduced the concentrations of PM2.5, PM10, SO2, NO2, and CO by 9.87 μg/m3, 17.31 μg/m3, 0.75 μg/m3, 9.03 μg/m3, and 0.07 mg/m3, respectively. The number of avoided premature deaths due to PM2.5 reduction was estimated to be 35,342. The resulting economic benefits totaled 18.86 billion US dollars. We investigated the reasons for the air quality improvement in these ten cities and found the "3 + 11" policy has had a great impact on air quality. Compared with the first COVID-19 lockdown in early 2020, the effect of the lockdown in 2022 was smaller. These findings demonstrated that reductions in anthropogenic emissions would achieve substantial air quality improvement and health benefits. This paper re-emphasized continuous efforts to improve air quality are essential to protect public health. [ABSTRACT FROM AUTHOR]

To explore the relationship between air pollution and total factor productivity and new pathways, This paper examines the impact of air pollution on total factor productivity of A-share listed companies in Shanghai and Shenzhen between 2015 and 2019. It investigates this relationship by considering two pathways: investor sentiment and government attention. The findings indicate that air pollution suppresses total factor productivity of firms. However, air pollution stimulates investor sentiment, which in turn increases R&D investment and total factor productivity, reducing to some extent the dampening effect of air pollution on total factor productivity. There exists a notable positive correlation between air quality and government attention, acting as a mediating variable. This implies that air pollution has the potential to capture the attention of governmental entities, leading to the implementation of appropriate measures aimed at managing and mitigating the occurrence of air pollution caused by industrial enterprises.And the relevant governments should formulate a series of policies to meet the different needs of different enterprises. These two approaches have varying impacts depending on the type of enterprises, thus governments should develop laws to cater to the various demands of different types of enterprises. [ABSTRACT FROM AUTHOR]

Accurate and fine-grained individual air quality index (IAQI) prediction is the basis of air quality index (AQI), which is of great significance for air quality control and human health. Traditional approaches, such as time series, recurrent neural network or graph convolutional network, cannot effectively integrate spatial-temporal and meteorological factors and manage the dynamic edge relationship among scattered monitoring stations. In this paper, a ST-CCN-IAQI model is proposed based on spatial-temporal causal convolution networks. Both the spatial effects of multi-source air pollutants and meteorological factors were considered via spatial attention mechanism. Time-dependent features in the causal convolution network were extracted by stacked dilated convolution and time attention. All the hyper-parameters in ST-CCN-IAQI were tuned by Bayesian optimization. Shanghai air monitoring station data were employed with a series of baselines (AR, MA, ARMA, ANN, SVR, GRU, LSTM and ST-GCN). Final results showed that: (1) For a single station, the RMSE and MAE values of ST-CCN-IAQI were 9.873 and 7.469, decreasing by 24.95% and 16.87% on average, respectively. R2 was 0.917, with an average 5.69% improvement; (2) For all nine stations, the mean RMSE and MAE of ST-CCN-IAQI were 9.849 and 7.527, respectively, and the R2 value was 0.906. (3) Shapley analysis showed PM10, humidity and NO2 were the most influencing factors in ST-CCN-IAQI. The Friedman test, under different resampling, further confirmed the advantage of ST-CCN-IAQI. The ST-CCN-IAQI provides a promising direction for fine-grained IAQI prediction. [ABSTRACT FROM AUTHOR]

In this paper, the LSTM model is used to predict the PM2.5 concentrations in five representative Chinese cities with the GDP exceeding 1 trillion Yuan, including Beijing, Chengdu, Shanghai, Shenzhen and Wuhan. The PM2.5 concentration data in 2015-2017 are selected for training, and the results are optimized to achieve an efficient solution by adjusting the parameters. Based on the optimized solution, a test is carried out to predict the PM2.5 concentration in 2018, and the results are compared with the real value obtained from the monitoring centre. According to the comparison results, the correlation coefficient of Wuhan and Chengdu is 0.86724 and 0.80070, which are the highest in these five cities. While the correlation coefficient of Shenzhen and Shanghai, are 0.78225, 0.72147, Beijing, as the capital city of China achieved the lowest correlation coefficient which is 0.64118. The LSTM-based predictive model has relatively good reliability and transferability. More effective predictive results can be achieved by implementing deep learning to analyse PM2.5 concentration. [ABSTRACT FROM AUTHOR]

Air pollution is an important factor threatening human health. Inhalation of pollutants can cause damage to the cardiovascular system, leading to increased morbidity and mortality of cardiovascular diseases. This paper selects six pollution factors stipulated by the national air quality standards, collects the air quality index data of Shanghai from 2014 to 2017 and the emergency data of cardiovascular disease in a tertiary hospital in the city during the same period, and conducts relevant analysis to explore different air quality conditions, characteristics, and the relationship between pollutants and cardiovascular disease visits. The results show that the seasonal changes of major air pollutants are related to the incidence of cardiovascular disease. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to predict the short-term demand of free-floating bike sharing (FFBS) using deep learning approach. The FFBS trip data in Shanghai city are collected from the Mobike Company. Other datasets such as weather data and air quality data are also collected. The spatiotemporal patterns of FFBS demand indicates that the weekday rides exhibit an obvious commuting pattern while the weekend rides are usually involved with various trip purposes. Then, a hybrid deep learning neural network (HDL-net) is developed to predict the short-term FFBS demand for different time intervals including 15, 20 and 30 min. The proposed HDL-net exhibits better performance on morning peak than the evening peak and non-peak hours for all the three time intervals. Moreover, five benchmark methods are also used to compare with the proposed HDL-net. The results suggest that the proposed hybrid deep learning framework outperform the benchmarks in the prediction performance for all three time intervals. The results of this study could provide insightful suggestions for transportation authorities to develop effective rebalancing strategies and bike lanes planning schemes to promote the service level of cycling in an urban city. [ABSTRACT FROM AUTHOR]

Observations of atmospheric conditions and processes in cities are fundamental to understanding the interactions between the urban surface and weather/climate, improving the performance of urban weather, air quality, and climate models, and providing key information for city end users (e.g., decision makers, stakeholders, public). In this paper, Shanghai's Urban Integrated Meteorological Observation Network (SUIMON) and some examples of intended applications are introduced. Its characteristics include being multipurpose (e.g., forecast, research, service), multifunction (e.g., high-impact weather, city climate, special end users), multiscale (e.g., macro/meso, urban, neighborhood, street canyon), multivariable (e.g., thermal, dynamic, chemical, biometeorological, ecological), and multiplatform (e.g., radar, wind profiler, ground based, satellite based, in situ observation/sampling). Underlying SUIMON is a data management system to facilitate exchange of data and information. The overall aim of the network is to improve coordination strategies and instruments, to identify data gaps based on science- and user-driven requirements, and to intelligently combine observations from a variety of platforms by using a data assimilation system that is tuned to produce the best estimate of the current state of the urban atmosphere. [ABSTRACT FROM AUTHOR]

Abstract: China is currently facing the threat of serious air pollution and has adopted a series of governance policies. However, the fact that PM 2.5 indicator in some regions often reached far higher than the official threshold since 2015 has reflected that China's current Air Quality Index system for measuring and evaluating air quality is still not perfect. Therefore, this paper extended the current air quality indicator system officially used by China. Using improved Entropy-weighted Factor Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) Method, we calculated the air quality scores of 26 cities in China's most economically developed region - the Yangtze River Delta region based on their daily average concentration numbers of main air pollutants from February 2015 to January 2018. Based on the air quality calculation result, we further studied the effectiveness of air pollution control policies of this region by using the Box-Jenkins Model with fuzzy strategy parameter adjustment. Our findings are: the pollution control policy is more effective to Shanghai, Jiangsu and Zhejiang. The air quality score of Shanghai has improved by 51.07% compared with its level before the policy. Meanwhile, the air quality improvement in Zhejiang province has shown the characteristics of "Campaign-style Governance" right before the G20 Summit, especially that in Hangzhou, the host city of the Summit. On the other hand, maybe due to late start of air pollution control programs, the cities of Anhui province haven't shown obvious improvements in air quality, and some cities have even seen deterioration in air quality scores during our study period. Graphical abstract Image 1 Highlights • We extended the current air quality indicator system officially used by China. • We used the Entropy-weighted Factor Method and TOPSIS Method for assessing. • We studied the control policies by Box-Jenkins Model with fuzzy strategy parameter. • Results and corresponding reasons were analyzed according to provinces and cities. • Policy recommendations for air pollution control in YRD were proposed. [ABSTRACT FROM AUTHOR]

In March 2022, a new wave of COVID-19 outbreak occurred in Shanghai due to the widespread transmission of the Omicron variant. A two-month citywide lockdown was implemented from April 1st to May 31st, adopting measures such as zone-based classification and grid management. This unique social event provided an "ideal air quality experiment" for pollution research. The rapid reduction in economic activities during the lockdown had many positive impacts on the environment, leading to overall improvements in air quality. Particularly, the concentration of NOx, one of the precursors to O3, significantly decreased. However, O3, as a typical secondary pollutant, showed a noticeable increase. This study uses the WRF-CAMx-OSAT air quality model method to analyze the source of O3 pollution in Shanghai from April to May 2022. The impact of O3 precursor control, sector sources, and regional contributions on the formation of O3 pollution in Shanghai is analyzed in depth. During the pandemic lockdown period, it was found that, in Shanghai, the overall O3 levels were controlled by VOCs (Volatile Organic Compounds), and controlling VOCs proved to be an effective measure in reducing O3 concentrations in Shanghai. Compared with the same period in 2021, the proportion of road traffic sources contributing to ozone concentration has significantly decreased from 70.61% to 64.3%, but they are still the largest contributor. The contribution of industrial emissions to the ozone concentration has significantly risen from 20.71% to 26.36%, making them still the second largest contributor. Industrial and traffic sources are emission sources that require particular attention. The contribution ratio of local sources to external transport is about 7:3, which is higher than the ratio of local sources to external transport in the same period of 2021, which is about 6:4. The local ozone is the main source of ozone concentration in Shanghai, and controlling local source emissions is the key to controlling ozone concentration in the Shanghai area. When excluding the impact of long-range transport, the main areas contributing to O3 formation from local sources are Baoshan District, Jiading District, Qingpu District, and Chongming District, accounting for approximately 41.12% of the total absolute contribution. Different source regions exhibit significant spatial variations in their contributions to the ozone concentration. Through these studies, we aim to provide scientific support and control suggestions for the precise prevention and control of O3 pollution in Shanghai. [ABSTRACT FROM AUTHOR]

The COVID-19 prevention and control measures are taken by China's government, especially traffic restrictions and production suspension, had spillover effects on air quality improvement. These effects differed among cities, but these differences have not been adequately studied. To provide more knowledge, we studied the air quality index (AQI) and five air pollutants (PM2.5, PM10, SO2, NO2, and O3) before and after the COVID-19 outbreak in Shanghai, Wuhan, and Tangshan. The pollution data from two types of monitoring stations (traffic and non-traffic stations) were separately compared and evaluated. We used monitoring data from the traffic stations to study the emission reduction caused by traffic restrictions. Based on monitoring data from the non-traffic stations, we established a difference-in-difference model to study the emission reduction caused by production suspension. The COVID-19 control measures reduced AQI and the concentrations of all pollutants except O3 (which increased greatly), but the magnitude of the changes differed among the three cities. The control measures improved air quality most in Wuhan, followed by Shanghai and then Tangshan. We investigated the reasons for these differences and found that differences in the characteristics of these three types of cities could explain these differences in spillover effects. Understanding these differences could provide some guidance and support for formulating differentiated air pollution control measures in different cities. For example, whole-process emission reduction technology should be adopted in cities with the concentrated distribution of continuous process enterprises, whereas vehicles that use cleaner energy and public transport should be vigorously promoted in cities with high traffic development level. [ABSTRACT FROM AUTHOR]

The COVID-19 lockdown contributes to the improvement of air quality. Most previous studies have attributed this to the reduction of human activity while ignoring the meteorological changes, this may lead to an overestimation or underestimation of the impact of COVID-19 lockdown measures on air pollution levels. To investigate this issue, we propose an XGBoost-based model to predict the concentrations of PM2.5 and PM10 during the COVID-19 lockdown period in 2022, Shanghai, and thus explore the limits of anthropogenic emission on air pollution levels by comprehensively employing the meteorological factors and the concentrations of other air pollutants. Results demonstrate that actual observations of PM2.5 and PM10 during the COVID-19 lockdown period were reduced by 60.81% and 43.12% compared with the predicted values (regarded as the period without the lockdown measures). In addition, by comparing with the time series prediction results without considering meteorological factors, the actual observations of PM2.5 and PM10 during the lockdown period were reduced by 50.20% and 19.06%, respectively, against the predicted values during the non-lockdown period. The analysis results indicate that ignoring meteorological factors will underestimate the positive impact of COVID-19 lockdown measures on air quality. [ABSTRACT FROM AUTHOR]

Growing evidence links intra-urban gradients in atmospheric fine particles (PM2.5), a complex and variable cocktail of toxic chemicals, to adverse health outcomes. Here, we propose an improved hierarchical deep learning model framework to estimate the hourly variation of PM2.5 mass concentration at the street level. By using a full-year monitoring data (including meteorological parameters, hourly concentrations of PM2.5, and gaseous precursors) from multiple stations in Shanghai, the largest city in China, as a training dataset, we first apply a convolutional neural network to obtain cross-domain and time-series features so that the inherent features of air quality and meteorological data associated with PM2.5 can be effectively extracted. Next, a Gaussian weight calculation layer is used to determine the potential interaction effects between different regions and neighboring stations. Finally, a long and short-term memory model layer is used to efficiently extract the temporal evolution characteristics of PM2.5 concentrations from the previous output layer. Further comparative analysis reveals that our proposed model framework significantly outperforms previous benchmark methods in terms of the stability and accuracy of PM2.5 prediction, which has important implications for the intra-urban health assessment of PM2.5-related pollution exposures. [ABSTRACT FROM AUTHOR]

China implemented a strict lockdown policy to prevent the spread of COVID-19 in the worst-affected regions, including Wuhan and Shanghai. This study aims to investigate impact of these lockdowns on air quality index (AQI) using a deep learning framework. In addition to historical pollutant concentrations and meteorological factors, we incorporate social and spatio-temporal influences in the framework. In particular, spatial autocorrelation (SAC), which combines temporal autocorrelation with spatial correlation, is adopted to reflect the influence of neighbouring cities and historical data. Our deep learning analysis obtained the estimates of the lockdown effects as − 25.88 in Wuhan and − 20.47 in Shanghai. The corresponding prediction errors are reduced by about 47% for Wuhan and by 67% for Shanghai, which enables much more reliable AQI forecasts for both cities. [ABSTRACT FROM AUTHOR]

To investigate the physical and chemical processes of fine particle matter (PM) at the mid-upper planetary boundary layer (PBL), we conducted 1-year continuous measurements of fine PM, the chemical composition of non-refractory submicron aerosol (NR-PM 1) , and some gas species (including sulfur dioxide, nitrogen oxides, and ozone) at an opening observatory (∼ 600 m) at the top of Shanghai Tower (SHT), which is China's first and the world's second highest building located in the typical financial central business district of Shanghai, China. This is the first report on the characteristics of fine particles based on continuous and sophisticated online measurements at the mid-upper level of the urban PBL. The observed PM 2.5 and PM 1 mass concentrations at SHT were 25.5 ± 17.7 and 17.3 ± 11.7 µ g m -3 , respectively. Organics, nitrate (NO 3) , and sulfate (SO 4) occupied the first three leading contributions to NR-PM 1 at SHT, accounting for 35.8 %, 28.6 %, and 20.8 %, respectively. The lower PM 2.5 concentration was observed at SHT by 16.4 % compared with that near the surface during the observation period. It was attributed to the decreased nighttime PM 2.5 concentrations (29.4 % lower than the surface) at SHT in all seasons due to the complete isolations from both emissions and gas precursors near the surface. However, daytime PM 2.5 concentrations at SHT were 12.4 %–35.1 % higher than those near the surface from June to October, resulted from unexpected larger PM 2.5 levels during early to middle afternoon at SHT than at the surface. We suppose the significant chemical production of secondary aerosols existed in the mid-upper PBL, because strong solar irradiance, adequate gas precursors (e.g., NOx), and lower temperature were observed at SHT, favorable for both photochemical production and gas-to-particle partitioning. This was further demonstrated by the significant increasing rate of oxygenated organic aerosols and NO 3 observed at SHT during 08:00–12:00 in spring (7.4 % h -1 and 12.9 % h -1) , fall (9.3 % h -1 and 9.1 % h -1) , and summer (13.0 % h -1 and 11.4 % h -1) , which cannot be fully explained by vertical mixing. It was noted that extremely high NO 3 was observed at SHT both in daytime and nighttime in winter, accounting for 37.2 % in NR-PM 1 , suggesting the efficient pathway from heterogeneous and gas oxidation formation. Therefore, we highlight the priority of NOx reduction in Shanghai for the further improvement of air quality. This study reported greater daytime PM 2.5 concentrations at the height of 600 m in the urban PBL compared with surface measurement, providing insight into their potential effects on local air quality, radiation forcing, and cloud and/or fog formations. We propose that the efficient production of secondary aerosol in the mid-upper PBL should be cognized and explored more comprehensively by synergetic observations in future. [ABSTRACT FROM AUTHOR]

Municipal solid waste (MSW) sorting not only reduces the total quantity of domestic waste but also has positive effects on urban air quality. In this study, using a careful identification strategy and air quality data at the monitoring station level in Shanghai, we estimate the causal effect of the MSW sorting policy on urban air quality. The results show that after the MSW sorting policy was implemented, the air quality index (AQI), nitrogen dioxide (NO2) and carbon monoxide (CO) decreased significantly by 2.71%, 2.07% and 3.62%, respectively. We also find a positive spillover effect from the Shanghai MSW sorting policy on the air quality of adjacent cities. The implementation of this policy has triggered changes in residents' behaviors. However, the government needs further efforts to maintain the sustainability of MSW sorting policies. [ABSTRACT FROM AUTHOR]

Abstract: A liquid desiccant based independent humidity control (IHC) air-conditioning system driven by heat pumps is presented in this paper. The system consists of a liquid desiccant fresh air processor and a high-temperature chilled water system. The operating principles of the fresh air processor and the whole system are presented in detail. Based on the in situ system installed in an ecological building of Shanghai Research Institute of Building Sciences (SRIBS), a performance investigation was performed in summer, 2007. The measurement results show that (1) the indoor temperature and humidity both are in the specified range; (2) the supply water temperature of the high-temperature refrigerator is higher than the corresponding indoor air dew point temperature, so no condensed water would be formed on the fan-coil unit (FCU) surface; and (3) the average coefficients of performances (COPs) of the fresh air processor and the high-temperature refrigerator are 6.24 and 4.38, respectively, and the average COP of the whole IHC system is 5.28. [ABSTRACT FROM AUTHOR]

Green roof and cool roof are two popular strategies to mitigate urban heat island especially in the summer time. The meteorological effect (cooling on the near surface temperature) of the two mitigation strategies has been proved by lots of studies while the environmental effect (effect on urban air quality) is still largely unknown. In this paper the WRF-Chem model coupled with a single layer urban canopy model is used to simulate three heat wave and heavy ozone pollution episodes in Shanghai, China. Sensitivity experiments of green roof and cool roof are carried out and the results show that both green roof and cool roof can effectively mitigate urban heat island, and their cooling efficiencies are similar even their cooling mechanisms are different. All of them can reduce the lasting time and area of high temperature and extreme hot, but the ozone pollution may become more serious. According to the different ambient wind field and ozone distribution, these three ozone pollution episodes can be divided into two conditions: urban dome and urban plume. The decrease of urban surface temperature can inhibit the turbulent activities and reduce the height of the boundary layer; the concentration of ozone in urban Zone A (below 750 m height) increases, but decreases in urban Zone B (750 m-2km height) for urban dome. By calculating the ozone fluxes at different heights, we find that the urban heat island circulation can transport surface ozone into city, horizontally; while in Zone B, the horizontal divergence transport ozone out of the city, and green roof and cool roof can decrease the temperature difference between urban and suburban, which can inhibit this ozone transportation of urban heat island circulation. • Meteorological and environmental effects of green roof and cool roof investigated. • Cool roof and green roof have similar cooling efficiencies. • Ozone pollution becomes more serious due to the modification on urban heat island circulation. [ABSTRACT FROM AUTHOR]

Air quality warning and forecasting systems are usually based on numerical chemical transport models (CTMs). Those dynamic models perform predictions by simulating the life cycles of the atmospheric components, including emission, transport and removal. However, the accuracy of these CTMs are still limited because of many imperfections, e.g., uncertainties in the input sources such as emission inventories, wind fields, boundary conditions, as well as insufficient knowledge about the atmospheric dynamics themselves. All these will mislead the CTM prediction constantly, or in a systematic way. In this paper, an approach based on machine learning is applied to predict model bias in the CTM. It is then combined with the CTM for formulating a hybrid forecast system. To our knowledge, it is the first time that machine learning methods are used in this way. The hybrid system is tested on the fine particular matter (PM 2.5) prediction in Shanghai, China. The results showed that machine learning can be an effective tool to improve the accuracy of CTM prediction. In case of short term PM 2.5 forecast (forecast length less than 12 h), statistical metrics of the root mean square error, mean absolute error, mean absolute percentage error as well as the air quality rank predicted accuracy all show the forecast skill is remarkably improved; while for long term prediction, improvement is not ensured. • An approach based on machine learning is applied to predict bias in CTMs, this is the first time that it is used this way. • Machine learning is an effective tool to improve the accuracy of CTM prediction, especially in case of short term forecast. • This approach requires less computing power than other model bias correction techniques like data assimilation. [ABSTRACT FROM AUTHOR]

In the past few years, China's air quality, particularly PM2.5 concentrations, has received extensive attention. China is increasingly dependent on imported oil, and the international oil price fluctuations influence the air quality by two paths. A rise in oil prices puts pressure on the economy and reduces energy consumption, which could improve air quality. However, the substitution effect by high oil prices tends to increase the use of environmentally unfriendly energies, which worsens air quality. In this study, the authors employ wavelet analysis to determine how international oil price fluctuations affect PM2.5 concentrations in China. The authors process a sample of 12 typical Chinese cities, which are discretely distributed in the northeast, north, east, central, south, and southwest of China. The results show that in most cities international crude oil prices are positively correlated to the PM2.5 concentrations in the short term (1–4 months) and that the fluctuations in oil prices are usually ahead of the changes of PM2.5 concentrations. It is more pronounced in industrially developed cities such as in Shanghai. An extension of the study to include the country data yields more consistent findings. Empirical analysis indicates that, in the short run, the substitution effect caused by oil price fluctuations exerts a stronger impact on PM2.5 concentrations. [ABSTRACT FROM AUTHOR]

To understand the characteristics and changes of baseline ozone (O 3) in oceanic air in eastern China, a 6-year measurement of O 3 concentration was conducted from 1 January 2012 to 15 September 2017 at a remote offshore station located on Sheshan Island (SSI) near the megacity of Shanghai. The observed monthly mean O 3 concentrations at SSI ranged from 33.4 to 61.4 ppbv during the study period, which were about 80 % and 12 % higher, respectively, than those measured at downtown and rural sites in Shanghai. Compared to the remarkable O 3 increases observed at urban and rural sites in Shanghai, observed O 3 concentrations at SSI exhibited statistically insignificant increasing changes (1.12 ppbv yr -1 , α>0.10) during the observation period, suggesting less impacts of anthropogenic emissions on O 3 levels in oceanic air. In addition, an insignificant decreasing change (- 0.72 ppbv yr -1 , α>0.10) was detected in O 3 concentrations at SSI in September and October when the influence of regional transport was minimum throughout the year, providing a good proxy to study the baseline oxidation capacity of the oceanic atmosphere. City plumes from Shanghai usually carried higher levels of NO x , resulting in decreased O 3 concentrations at SSI during southwesterly and westerly winds. However, In MAM (March–May) and JJA (June–August), due to the enhanced production of oxygenated volatile organic compounds, O 3 could be continuously produced during daytime in aged city plumes, resulting in elevated O 3 concentrations transported to SSI. The impacts of the offshore O 3 on O 3 levels in Shanghai are quantified during an easterly wind dominant episode (1–30 September 2014) using the WRF-Chem model (Weather Research and Forecasting model coupled with Chemistry). Sensitivity results suggest that O 3 in the oceanic air inflows can lead to 20 %–30 % increases in urban O 3 concentrations, which should be crucially considered in dealing with urban O 3 pollution in large coastal cities like Shanghai. [ABSTRACT FROM AUTHOR]

Official data on daily PM2.5, PM10, SO2, NO2, CO, and maximum 8-h average O3 (O3_8h) concentrations from January 2015 to December 2018 were evaluated and air pollution status and dynamics in Shanghai municipality were examined. Factors affecting air quality, including meteorological factors and socio-economic indicators, were analyzed. The main findings were that: (1) Overall air quality status in Shanghai municipality has improved and number of days meeting 'Chinese ambient air quality standards' (CAAQS) Grade II has increased. (2) The most frequent major pollutant in Shanghai municipality is O3 (which exceeded the standard on 110 days in 2015, 84 days in 2016, 126 days in 2017, 113 days in 2018), followed by PM2.5 (120days in 2015, 104 days in 2016, 67 days in 2017, 61 days in 2018) and NO2 (50 days in 2015, 67 days in 2016, 79 days in 2017, 63 days in 2018). (3) PM2.5 pollution in winter and O3 pollution in summer are the main air quality challenges in Shanghai municipality. (4) Statistical analysis suggested that PM2.5, PM10, SO2 and NO2 concentrations were significantly negatively associated with precipitation (Prec) and atmosphere temperature (T) (p < 0.05), while the O3 concentration was significantly positively associated with Prec and T (p < 0.05). Lower accumulation of PM, SO2, NO2, and CO and more serious O3 pollution were revealed during months with higher temperature and more precipitation in Shanghai. The correlation between the socio-economic factors and the air pollutants suggest that further rigorous measures are needed to control PM2.5 and that further studies are needed to identify O3 formation mechanisms and control strategies. The results provide scientific insights into meteorological factors and socio-economic indicators influencing air pollution in Shanghai. [ABSTRACT FROM AUTHOR]

Ammonia in the atmosphere is essential for the formation of fine particles that impact air quality and climate. Despite extensive prior research to disentangle the relationship between ammonia and haze pollution, the role of ammonia in haze formation in high ammonia-emitting regions is still not well understood. Aiming to better understand secondary inorganic aerosol (sulfate, nitrate, ammonium – SNA) formation mechanisms under high-ammonia conditions, 1-year hourly measurement of water-soluble inorganic species (gas and particle) was conducted at a rural supersite in Shanghai. Exceedingly high levels of agricultural ammonia, constantly around 30 µ g m -3 , were observed. We find that gas-particle partitioning of ammonia (ε (NH4+)), as opposed to ammonia concentrations, plays a critical role in SNA formation during the haze period. From an assessment of the effects of various parameters, including temperature (T), aerosol water content (AWC), aerosol pH, and activity coefficient, it seems that AWC plays predominant regulating roles for ε (NH4+). We propose a self-amplifying feedback mechanism associated with ε (NH4+) for the formation of SNA, which is consistent with diurnal variations in ε (NH4+) , AWC, and SNA. Our results imply that a reduction in ammonia emissions alone may not reduce SNA effectively, at least at rural agricultural sites in China. [ABSTRACT FROM AUTHOR]

The Chinese government launched the Air Pollution Prevention and Control Action Plan in 2013, and various stringent measures have since been implemented, which have resulted in significant decreases in emissions and ambient concentrations of primary pollutants such as SO2 , NOx , and particulate matter (PM). However, surface ozone (O3) concentrations have still been increasing in urban areas across the country. In a previous analysis, we examined in detail the roles of meteorological variation during 2013–2017 in the summertime surface O3 trend in various regions of China. In this study, we evaluated the effect of changes in multi-pollutant emissions from anthropogenic activities on O3 levels during the same period by using an up-to-date regional chemical transport model (WRF-CMAQ) driven by an interannual anthropogenic emission inventory. The Community Multiscale Air Quality (CMAQ) model was improved with regard to heterogeneous reactions of reactive gases on aerosol surfaces, which led to better model performance in reproducing the ambient concentrations of those gases. The model simulations showed that the maximum daily 8 h average (MDA8) O3 mixing ratio in urban areas increased by 0.46 ppbv per year (ppbva-1) (p=0.001) from 2013 to 2017. In contrast, a slight decrease in MDA8 O3 by 0.17 ppbva-1 (p=0.005) in rural areas was predicted, mainly attributable to the NOx emission reduction. The effects of changes in individual pollutant emissions on O3 were also simulated. The reduction of NOx emission increased the O3 levels in urban areas due to the nonlinear NOx and volatile organic compound (VOC) chemistry and decreasing aerosol effects; the slight increase in VOC emissions enhanced the O3 levels; the reduction of PM emissions increased the O3 levels by enhancing the photolysis rates and reducing the loss of reactive gases on aerosol surfaces; and the reduction of SO2 emissions resulted in a drastic decrease in sulfate concentrations, which increased O3 through aerosol effects. In contrast to the unfavorable effect of the above changes in pollutant emissions on efforts to reduce surface O3 , the reduction of CO emissions did help to decrease the O3 level in recent years. The dominant cause of increasing O3 due to changes in anthropogenic emissions varied geographically. In Beijing, NOx and PM emission reductions were the two largest causes of the O3 increase; in Shanghai, the reduction of NOx and increase in VOC emissions were the two major causes; in Guangzhou, NOx reduction was the primary cause; in Chengdu, the PM and SO2 emission decreases contributed most to the O3 increase. Regarding the effects of decreasing concentrations of aerosols, the drop in heterogeneous uptake of reactive gases – mainly HO2 and O3 – was found to be more important than the increase in photolysis rates. The adverse effect of the reductions of NOx , SO2 , and PM emissions on O3 abatement in Beijing, Shanghai, Guangzhou, and Chengdu would have been avoided if the anthropogenic VOCs emission had been reduced by 24 %, 23 %, 20 %, and 16 %, respectively, from 2013 to 2017. Our analysis revealed that the NOx reduction in recent years has helped to contain the total O3 production in China. However, to reduce O3 levels in major urban and industrial areas, VOC emission controls should be added to the current NOx - SO2 -PM policy. [ABSTRACT FROM AUTHOR]

Growing shipping activities in port areas have generated negative impacts on climate, air quality and human health. To better evaluate the environmental impact of ship emissions, an experimental characterization of air pollution from ships was conducted in Shanghai Port in the summer of 2016. The ambient concentrations of gaseous NO, NO2 , SO2 and O3 in addition to fine particulate matter concentrations (PM 2.5), particle size distributions and the chemical composition of individual particles from ship emission were continuously monitored for 3 months. Ship emission plumes were visible at the port site in terms of clear peaks in the gaseous species and particulate matter concentrations. The SO2 and vanadium particle numbers were found to correlate best with ship emissions in Shanghai Port. Single-particle data showed that ship emission particles at the port site mainly concentrated in a smaller size range (<0.4 µ m), where their number contributions were more important than their mass contributions to ambient particulate matter. The composition of ship emission particles at the port site suggested that they were mostly freshly emitted particles: their mass spectra were dominated by peaks of sulfate, elemental carbon (EC), and trace metals such as V, Ni, Fe and Ca, in addition to displaying very low nitrate signals. The gaseous NOx composition in some cases of plumes showed evidence of atmospheric transformation by ambient O3 , which subsequently resulted in O3 depletion in the area. Quantitative estimations in this study showed that ship emissions contributed 36.4 % to SO2 , 0.7 % to NO, 5.1 % to NO2 , -0.9 % to O3 , 5.9 % to PM 2.5 and 49.5 % to vanadium particles in the port region if land-based emissions were included, and 57.2 % to SO2 , 71.9 % to NO, 30.4 % to NO2 , -16.6 % to O3 , 27.6 % to PM 2.5 and 77.0 % to vanadium particles if land-based emissions were excluded. [ABSTRACT FROM AUTHOR]

The Yangtze River Delta (YRD) and the megacity of Shanghai are host to one of the busiest port clusters in the world; the region also suffers from high levels of air pollution. The goal of this study was to estimate the contributions of shipping to regional emissions, air quality, and population exposure and to characterize the importance of the geographic spatiality of shipping lanes and different types of ship-related sources for the baseline year of 2015, which was prior to the implementation of China's Domestic Emission Control Areas (DECAs) in 2016. The WRF-CMAQ model, which combines the Weather Research and Forecasting model (WRF) and the Community Multi-scale Air Quality (CMAQ) model, was used to simulate the influence of coastal and inland-water shipping, port emissions and ship-related cargo transport on air quality and on the population-weighted concentrations (which is a measure of human exposure). Our results showed that the impact of shipping on air quality in the YRD was primarily attributable to shipping emissions within 12 NM (nautical miles) of shore, but emissions coming from the coastal area between 24 and 96 NM still contributed substantially to ship-related PM 2.5 concentrations in the YRD. The overall contribution of ships to the PM 2.5 concentration in the YRD could reach 4.62 µ g m -3 in summer when monsoon winds transport shipping emissions onshore. In Shanghai city, inland-water going ships were major contributors (40 %–80 %) to the shipping impact on urban air quality. Given the proximity of inland-water ships to the urban populations of Shanghai, the emissions of inland-water ships contributed more to population-weighted concentrations. These research results provide scientific evidence to inform policies for controlling future shipping emissions; in particular, in the YRD region, expanding the boundary of 12 NM from shore in China's current DECA policy to around 100 NM from shore would include most of shipping emissions affecting air pollutant exposure, and stricter fuel standards could be considered for the ships on inland rivers and other waterways close to residential regions. [ABSTRACT FROM AUTHOR]

Ambient daytime and nighttime PM2.5 (particulate matter with aerodynamic diameter less than 2.5 µm) and TSP (the total suspended particulates) samples were collected at two sites (named Pudong and Jinshan) in Shanghai. The concentrations of PM2.5 and TSP were lower at Pudong than at Jinshan. Higher PM2.5 and TSP concentrations were observed during daytime than nighttime for both sites. Carbonaceous aerosol and secondary sulfate were the most abundant components. Larger enrichment factor (EFs) of Zn, Pb, Cl, and S for Jinshan nighttime were observed than for other sampling periods. PM2.5 showed higher relative spatial uniformity (the coefficients of divergence, COD = 0.18) than TSP (COD = 0.23) during the sampling period. The variations of chemical components and the species ratios showed that the contributions of primary particulate emissions in Jinshan (industrial zone) were more significant than in Pudong (residential zone). [ABSTRACT FROM AUTHOR]

To investigate the vehicle induced air pollution situations both inside and outside the tunnel, the field measurement of the pollutants concentrations and its diurnal variations was performed inside and outside the Xiangyin tunnel in Shanghai from 13:00 on April 24th to 13:00 on April 25th, 2013. The highest hourly average concentrations of pollutants were quantified that CO, NO, NO2 and NOX inside the tunnel were 13.223 mg/m3, 1.829 mg/m3, 0.291 mg/m3 and 3.029 mg/m3, respectively, while the lowest ones were 3.086 mg/m3, 0.344 mg/m3, 0.080 mg/m3 and 0.619 mg/m3. Moreover, the concentrations of pollutants were higher during the daytime, and lower at night, which is relevant to the traffic conditions inside the tunnel. Pollutants concentrations inside the tunnel were much higher than those outside the tunnel. Then in a case of slow wind, the effect of wind is much smaller than the impact of pollution sources. Additionally, the PM2.5 concentrations climbed to the peak sharply (468.45 µg/m3) during the morning rush hours. The concentrations of organic carbon (OC) and elemental carbon (EC) in PM2.5 inside the tunnel were 37.09–99.06 µg/m3 and 22.69–137.99 µg/m3, respectively. Besides, the OC/EC ratio ranged from 0.72 to 2.19 with an average value of 1.34. Compared with the results of other tunnel experiments in Guangzhou and Shenzhen, China, it could be inferred that the proportion of HDVs through the Xiangyin tunnel is relatively lower. [ABSTRACT FROM AUTHOR]

We estimate the willingness to pay (WTP) of Beijing and Shanghai residents for improving the air quality of the two cities from their levels prior to the 2008 Beijing Olympic Games to the level achieved during the Olympics. The data are obtained from a contingent valuation study conducted through face-to-face interviews in June 2008 in Beijing and Shanghai prior to the Beijing Olympics, during which time there was intensive debate about Beijing's air quality. Residents in both cities are willing to pay more when they are more exposed to air pollution, when their disposable income increases, and when they have stronger beliefs that public opinion plays an important role in government policy making. Beijing residents are willing to pay more than Shanghai residents, due possibly to Beijing's poorer air quality. Overall, aggregate WTP for air quality improvement accounts for about 0.53% of the 2008 GDP in Beijing and 0.22% of the 2008 GDP in Shanghai. [ABSTRACT FROM AUTHOR]

Ship-exhausted air pollutants could cause negative impacts on air quality, climate change, and human health. Increasing attention has been paid to investigate the impact of ship emissions on air quality. However, the conclusions are often based on a specific year, the extent to which the inter-annual variation in meteorological conditions affects the contribution is not yet fully addressed. Therefore, in this study, the Weather Research and Forecast model and the Community Multiscale Air Quality model(WRF/CMAQ) were employed to investigate the inter-annual variations in ship-contributed PM2.5 from 2010 to 2019. The Yangtze River Delta (YRD) region in China was selected as the target study area. To highlight the impact of inter-annual meteorological variations, the emission inventory and model configurations were kept the same for the 10-year simulation. We found that: (1) inter-annual meteorological variation had an evident impact on the ship-contributed PM2.5 in most coastal cities around YRD. Taking Shanghai as an example, the contribution varied between 3.05 and 5.74 µg/m3, with the fluctuation rate of ~65%; (2) the inter-annual changes in ship's contribution showed a trend of almost simultaneous increase and decrease for most cities, which indicates that the impact of inter-annual meteorological variation was more regional than local; (3) the inter-annual changes in the northern part of YRD were significantly higher than those in the south; (4) the most significant inter-annual changes were found in summer, followed by spring, fall and winter. [ABSTRACT FROM AUTHOR]

Marine biogenic dimethyl sulfide (DMS) is an important natural source of sulfur in the atmosphere, which may play an important role in air quality. In this study, the WRF-CMAQ model is employed to assess the impact of DMS on the atmospheric environment at the regional scale of eastern coastal China and urban scale of Shanghai in 2017. A national scale database of DMS concentration in seawater is established based on the historical DMS measurements in the Yellow Sea, the Bohai Sea and the East China Sea in different seasons during 2009~2017. Results indicate that the sea-to-air emission flux of DMS varies greatly in different seasons, with the highest in summer, followed by spring and autumn, and the lowest in winter. The annual DMS emissions from the Yellow Sea, the Bohai Sea and the East China Sea are 0.008, 0.059, and 0.15 Tg S a−1, respectively. At the regional scale, DMS emissions increase atmospheric sulfur dioxide (SO2) and sulfate ( SO 4 2 − ) concentrations over the East China seas by a maximum of 8% in summer and a minimum of 2% in winter, respectively. At the urban scale, the addition of DMS emissions increase the SO2 and SO 4 2 − levels by 2% and 5%, respectively, and reduce ozone (O3) in the air of Shanghai by 1.5%~2.5%. DMS emissions increase fine-mode ammonium particle concentration distribution by 4% and 5%, and fine-mode nss- SO 4 2 − concentration distributions by 4% and 9% in the urban and marine air, respectively. Our results indicate that although anthropogenic sources are still the dominant contributor of atmospheric sulfur burden in China, biogenic DMS emissions source cannot be ignored. [ABSTRACT FROM AUTHOR]