China's Largest City-Wide Lockdown: How Extensively Did Shanghai COVID-19 Affect Intensity of Human Activities in the Yangtze River Delta?

COVID-19 has been the most widespread and far-reaching public health emergency since the beginning of the 21st century. The Chinese COVID-19 lockdown has been the most comprehensive and strict in the world. Based on the Shanghai COVID-19 outbreak in 2022, we analyzed the heterogeneous impact of the COVID-19 lockdown on human activities and urban economy using monthly nighttime light data. We found that the impact of lockdown on human activities in the Yangtze River Delta is very obvious. The number of counties in Shanghai, Jiangsu, Zhejiang and Anhui showing a downward trend of MNLR (Mean of Nighttime Light Radiation) is 100%, 97%, 99% and 85%, respectively. Before the outbreak of COVID-19, the proportion of counties with a downward trend of MNLR was 19%, 67%, 22% and 33%, respectively. Although the MNLR of some counties also decreased in 2019, the scope and intensity was far less than 2022. Under regular containment (2020 and 2021), MNLR in the Yangtze River Delta also showed a significant increase (MNLR change > 0). According to NLRI (Nighttime Light Radiation Influence), the Shanghai lockdown has significantly affected the surrounding provinces (Average NLRI < 0). Jiangsu is the most affected province other than Shanghai. At the same time, Chengdu-Chongqing, Guangdong–Hong Kong–Macao and the Triangle of Central China have no obvious linkage effect.

Characteristics of Surface Ozone and Nitrogen Oxides over a Typical City in the Yangtze River Delta, China

The Yangtze River Delta (YRD) is the most developed region in China. Influenced by intensive and complex anthropogenic activities, atmospheric pollution in this region is highly variable, and reports are sparse. In this study, a seven-year history of the atmospheric O3 and NOx mixing ratios over a typical city, Hangzhou, was presented to enrich the studies on air pollution in the YRD region. Our results revealed that the diurnal variation in NOx corresponded to traffic rush hours, while O3 was mainly impacted by photochemical reactions in the daytime. The weekend effect was significant for NOx, but inapparent for O3. Two O3 peaks in May and September were caused by seasonal atmospheric stability and climatic conditions. The lower NOx and higher O3 levels observed suggested direct effects from traffic restrictions and large-scale industrial shutdowns during the COVID-19 lockdown in 2020 compared with those in the periods before and after lockdown. The model simulation results showed that O3 mixing ratios were not only related to regional anthropogenic emissions but were impacted by air mass transportation from surrounding provinces and the China shelf seas. The NOx mixing ratios showed a decreasing trend, while the O3 mixing ratios showed the opposite trend from 2015 to 2021, which is indicative of the implementation of the Air Pollution Prevention and Control Acton Plan issued by the Chinese government in 2013. [ABSTRACT FROM AUTHOR] Copyright of Atmosphere is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Reliability-Oriented Route Generation Algorithm for Multimodal Transport: A Perspective from Supply Chain Reliability Enhancement

Sudden infectious diseases and other malignant events cause excessive costs in the supply chain, particularly in the transportation sector. This issue, along with the uncertainty of the development of global epidemics and the frequency of extreme natural disaster events, continues to provoke discussion and reflection. However, transport systems involve interactions between different modes, which are further complicated by the reliable coupling of multiple modes. Therefore, for the vital subsystem of the supply chain-multimodal transport, in this paper, a heuristic algorithm considering node topology and transport characteristics in a multimodal transport network (MTN): the Reliability Oriented Routing Algorithm (RORA), is proposed based on the super-network and improved k-shell (IKS) algorithm. An empirical case based on the Yangtze River Delta region of China demonstrates that RORA enables a 16% reduction in the boundary value for route failure and a reduction of about 60.58% in the route cost increase compared to the typical cost-optimal algorithm, which means that RORA results in a more reliable routing solution. The analysis of network reliability also shows that the IKS values of the nodes are positively correlated with the reliability of the MTN, and nodes with different modes may have different transport reliabilities (highest for highways and lowest for inland waterways). These findings inform a reliability-based scheme and network design for multimodal transportation. Practical Applications: Recently, the COVID-19 epidemic and the frequency of natural disasters such as floods have prompted scholars to consider transport reliability. Therefore, efficient and reliable cargo transportation solutions are crucial for the sustainable development of multimodal transport in a country or region. In this paper, a new algorithm is designed to obtain a reliability-oriented optimal routing scheme for multimodal transport. Using actual data from the Yangtze River Delta region of China as an example for experimental analysis, we obtain that: (1) the proposed algorithm is superior in terms of efficiency, accuracy, and route reliability, which means that the new algorithm can quickly find more reliable routing solutions in the event of urban transport infrastructure failures;and (2) highway hubs have the greatest transport reliability. Conversely, inland waterway hubs are the least reliable. The influence of national highways and railways on the multimodal transport system is unbalanced. These findings provide decision support to transport policymakers on reliability. For example, transport investments should be focused on building large infrastructure and increasing transport capacity, strengthening the connectivity of inland waterway hubs to hubs with higher transport advantages, and leveraging the role of large hubs. © 2023 American Society of Civil Engineers.

Airborne black carbon variations during the COVID-19 lockdown in the Yangtze River Delta megacities suggest actions to curb global warming.

Airborne black carbon is a strong warming component of the atmosphere. Therefore, curbing black carbon emissions should slow down global warming. The 2019 coronavirus pandemic (COVID-19) is a unique opportunity for studying the response of black carbon to the varied human activities, in particular due to lockdown policies. Actually, there is few knowledge on the variations of black carbon in China during lockdowns. Here, we studied the concentrations of particulate matter (PM2.5) and black carbon before, during, and after the lockdown in nine sites of the Yangtze River Delta in Eastern China. Results show 40-60% reduction of PM2.5 and 40-50% reduction of black carbon during the lockdown. The classical bimodal peaks of black carbon in the morning and evening rush hours were highly weakened, indicating the substantial decrease of traffic activities. Contributions from fossil fuels combustion to black carbon decreased about 5-10% during the lockdown. Spatial correlation analysis indicated the clustering of the multi-site black carbon concentrations in the Yangtze River Delta during the lockdown. Overall, control of emissions from traffic and industrial activities should be efficient to curb black carbon levels in the frame of a 'green public transit system' for mega-city clusters such as the Yangtze River Delta. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10311-021-01327-3.

Integrated quantitative evaluation framework of sustainable development – The complex case of the Yangtze River Delta

Sustainable development is central to the current societal functioning, whose complexity demands consideration on a regional scale. However, there are disparate methods to express sustainable development, many of which use qualitative analysis cumbersome for policy-makers. Previous studies focused on environmental, economic, and social impacts without fully considering the regulation mechanisms of the plethora of administrative bodies. To fill this research gap, this research establishes an integrated assessment framework involving four pillars: environment and ecology, society and culture, economics, and governance and policy. Further, indicator systems and quantitative analysis give comparable and objective results. The current study applied them to one of the most economically significant and developed Chinese regions, the Yangtze River Delta. The result shows a dynamic variation in regional sustainability from 2010 to 2019, indicating an annual increase. Although economic and societal development has been increasing steadily, environmental development has stagnated in the past two years, and the influencing policy has fluctuated dramatically. Our analysis was done in Shanghai, Jiangsu, Zhejiang, and Anhui. Even though all regions showed increasing sustainability, we observed an imbalance in regional sustainable development. Achieving a regional approach and enhanced regional coordination in the Yangtze River Delta is imperative and cannot be ignored by local, regional, and national policy-makers. More importantly, this study created a model capable of predicting the impact of the COVID-19 epidemic on regional sustainable development. The model showed that, compared with predicted values, a 6.65% decrease in the integrated sustainability index ensued, attributed to the pandemic in Zhejiang province. © 2022 Elsevier Ltd

Dramatic decline of observed atmospheric CO2 and CH4 during the COVID-19 lockdown over the Yangtze River Delta of China

The temporal variation of greenhouse gas concentrations in China during the COVID-19 lockdown in China is analyzed in this work using high resolution measurements of near surface △CO2, △CH4 and △CO concentrations above the background conditions at Lin'an station (LAN), a regional background station in the Yangtze River Delta region. During the pre-lockdown observational period (IOP-1), both △CO2 and △CH4 exhibited a significant increasing trend relative to the 2011-2019 climatological mean. The reduction of △CO2, △CH4 and △CO during the lockdown observational period (IOP-2) (which also coincided with the Chinese New Year Holiday) reached up to 15.0 ppm, 14.2 ppb and 146.8 ppb, respectively, and a reduction of △CO2/△CO probably due to a dramatic reduction from industrial emissions. △CO2, △CH4 and △CO were observed to keep declining during the post-lockdown easing phase (IOP-3), which is the synthetic result of lower than normal CO2 emissions from rural regions around LAN coupled with strong uptake of the terrestrial ecosystem. Interestingly, the trend reversed to gradual increase for all species during the later easing phase (IOP-4), with △CO2/△CO constantly increasing from IOP-2 to IOP-3 and finally IOP-4, consistent with recovery in industrial emissions associated with the staged resumption of economic activity. On average, △CO2 declined sharply throughout the days during IOP-2 but increased gradually throughout the days during IOP-4. The findings showcase the significant role of emission reduction in accounting for the dramatic changes in measured atmospheric △CO2 and △CH4 associated with the COVID-19 lockdown and recovery.

Association between Dairy Consumption and Psychological Symptoms: Evidence from a Cross-Sectional Study of College Students in the Yangtze River Delta Region of China.

BACKGROUND: Assessing the dairy consumption and psychological symptoms of Chinese college students as a reference for the mental health of Chinese college students. METHODS: A three-stage stratified whole-group sampling method was used to investigate dairy consumption and psychological symptoms among 5904 (2554 male students, accounting for 43.3% of the sample) college students in the Yangtze River Delta region. The mean age of the subjects was 20.13 ± 1.24 years. Psychological symptoms were surveyed using the Brief Questionnaire for the Assessment of Adolescent Mental Health. The detection rates of emotional problems, behavioral symptoms, social adaptation difficulties and psychological symptoms among college students with different dairy consumption habits were analyzed using chi-square tests. The association between dairy consumption and psychological symptoms was assessed using a logistic regression model. RESULTS: College students from the "Yangtze River Delta" region of China participated in the study, of which 1022 (17.31%) had psychological symptoms. The proportions of participants with dairy consumption of ≤2 times/week, 3-5 times/week, and ≥6 times/week were 25.68%, 42.09%, and 32.23%, respectively. Using dairy consumption ≥6 times/week as a reference, multifactor logistic regression analysis showed that college students with dairy consumption ≤2 times/week (OR = 1.42, 95% CI: 1.18, 1.71) were at higher risk of psychological symptoms (p < 0.001). CONCLUSION: During the COVID-19 pandemic, Chinese college students with lower dairy consumption exhibited higher detection rates of psychological symptoms. Dairy consumption was negatively associated with the occurrence of psychological symptoms. Our study provides a basis for mental health education and increasing knowledge about nutrition among Chinese college students.

Study of Spatio-Temporal Evolution, Integrated Prevention, and Control Measures of COVID-19 in the Yangtze River Delta.

OBJECTIVE: The study analyzes the spatial characteristics of the epidemic. It evaluates the effectiveness of its differentiated prevention and control policies implemented at different stages of the epidemic in the Yangtze River Delta. METHODS: The study divided the epidemic into 2 stages and analyzed the spatial evolution characteristics of the COVID-19 epidemic in the region by using Anselin Local Moran's I and standard deviation ellipse. RESULTS: In the first stage, the high value of confirmed cases was concentrated in the eastern and southern cities. The trajectory of the barycenter showed a V-shaped change characterized by a southward shift followed by a northward fluctuation. In contrast, the second stage was mainly concentrated in Jiangsu Province and Shanghai, and the Barycenter did not change over time. The diversified prevention and control measures enabled 'zero new cases' in the Yangtze River Delta within a month. CONCLUSION: The prevention and control policy implemented in the Yangtze River Delta has worked well. With the global pandemic of COVID-19, it is recommended that other countries follow the example of the Yangtze River Delta, tighten prevention policies, and speed up vaccination to avoid a rebound of the epidemic.

Assessing the ambient air quality patterns associated to the COVID-19 outbreak in the Yangtze River Delta: A random forest approach.

The novel coronavirus (COVID-19), first identified at the end of December 2019, has significant impacts on all aspects of human society. In this study, we aimed to assess the ambient air quality patterns associated to the COVID-19 outbreak in the Yangtze River Delta (YRD) region using a random forest (RF) model. To estimate the accuracy of the model, the cross-validation (CV), determination coefficient R2, root mean squared error (RMSE) and mean absolute error (MAE) were used. The results demonstrate that the RF model achieved the best performance in the prediction of PM10 (R2 = 0.78, RMSE = 8.81 µg/m3), PM2.5 (R2 = 0.76, RMSE = 6.16 µg/m3), SO2 (R2 = 0.76, RMSE = 0.70 µg/m3), NO2 (R2 = 0.75, RMSE = 4.25 µg/m3), CO (R2 = 0.81, RMSE = 0.4 µg/m3) and O3 (R2 = 0.79, RMSE = 6.24 µg/m3) concentrations in the YRD region. Compared with the prior two years (2018-19), significant reductions were recorded in air pollutants, such as SO2 (-36.37%), followed by PM10 (-33.95%), PM2.5 (-32.86%), NO2 (-32.65%) and CO (-20.48%), while an increase in O3 was observed (6.70%) during the COVID-19 period (first phase). Moreover, the YRD experienced rising trends in the concentrations of PM10, PM2.5, NO2 and CO, while SO2 and O3 levels decreased in 2021-22 (second phase). These findings provide credible outcomes and encourage the efforts to mitigate air pollution problems in the future.

Research on the Evaluation of the Integration Level of Emergency Logistics in the Yangtze River Delta Region

As a typical representative of the regional economies' integration, the Yangtze River Delta region presents the development trend of emergency logistics integration under the background of the global epidemic of COVID-19, especially the outbreak in Shanghai. A scientific evaluation of the integration level of regional emergency logistics is crucial to the accurate construction of a post-epidemic emergency logistics system in the Yangtze River Delta region. This paper defines regional integration of emergency logistics as two dimensions of high-quality development and equilibrium development, and constructs a regional emergency logistics integration level evaluation index system containing 14 indicators for four factors: emergency logistics infrastructure, resource support, information sharing and mechanisms. A two-stage evaluation model is used to evaluate the integration level of emergency logistics in the Yangtze River Delta region, and the results are compared with the Beijing-Tianjin-Hebei region. According to the results, the integration of emergency logistics in the Yangtze River Delta region is at a medium level, with the highest integration level of emergency logistics infrastructure, the lowest integration level of emergency logistics information sharing, and the medium integration level of emergency logistics resource support and emergency logistics institutional mechanism. The density of integrated transport network and the efficiency of single-vehicle freight completion have the highest and lowest integration levels in the Yangtze River Delta region, respectively. And the integrated development level of emergency logistics in the Yangtze River Delta region is better than that in the Beijing-Tianjin-Hebei region, but there is little difference between the two, mainly due to the high level of integration of emergency logistics infrastructure and emergency logistics resource support. © 2022 IEEE.

Impacts of COVID-19 pandemic on foreign trade intermodal transport accessibility: Evidence from the Yangtze River Delta region of mainland China.

We address the problem of the impacts of COVID-19 pandemic on foreign trade transport by introducing a foreign trade intermodal transport accessibility (FTITA) index. First, we present the definition of FTITA, which combines the convenience of transporting domestic cargoes to overseas regions by an international intermodal transport network and the trade attractiveness of the domestic cargoes in the overseas regions. Second, we analyze the path choice behaviors of domestic shippers and propose the measurement method of the FTITA index. Finally, using the 41 cities in the Yangtze River Delta region in mainland China as origins and eight overseas regions as destinations, we empirically analyze the impacts of COVID-19 pandemic on the FTITA. With the empirical study conducted in the prepandemic and postpandemic years, we analyzed the overall trends of the FTITAs from the YRD region to eight overseas regions, spatial patterns of the distributions of the FTITAs in the YRD region, rankings of average FTITA values for the top ten cities in the YRD region, and the FTITAs for different cargoes. The results indicate that the FTITAs of the YRD region in the prepandemic year are significantly higher than those in the postpandemic year. Moreover, in both the prepandemic and postpandemic years, the FTITAs to North America, Japan/South Korea, Europe, and Southeast Asia are significantly higher than those to Oceania, Middle East, South America, and Africa. Through analysis of the spatial patterns of the FTITAs across cities in the YRD region, we find that the cities with high FTITA are mainly close to Shanghai Port and Ningbo Port; the cities with middle-high FTITA are mainly located in southern Zhejiang and the regions along the Yangtze River; the cities with middle-low FTITA are mainly located in northern Jiangsu; and the cities with low FTITA are located in northern Anhui. Furthermore, comparing the impacts of COVID-19 pandemic on the FTITAs for different cargoes, we observe that COVID-19 has the least impact on foodstuffs and event cargoes. Our findings can guide decision makers in implementing policies for alleviating the impacts of COVID-19 pandemic on foreign trade transport and further promoting the sustainable development of port and shipping industries.

Estimating the spatial correlation and convergence of China's healthcare resources allocation: evidence from the Yangtze River Delta Region.

BACKGROUND: China's imbalanced allocation of healthcare resources mainly arises from urban-rural and intercity differences, the solution of which has been the goal of reforms during the past decades. Estimating the spatial correlation and convergence could help to understand the impact of China's fast-evolving medical market and the latest healthcare reforms. METHODS: The entropy weight method was used to construct a healthcare resource supply index (HRS) by using data of 41cities in a cluster in the Yangtze River Delta (YRD) from 2007 to 2019. The Dagum Gini coefficient, kernel density estimation, Moran's I, and LISA cluster map were used to characterize the spatiotemporal evolution and agglomeration of healthcare resources, and then a spatial panel model was used to perform ß convergence estimation by incorporating the spatial effect, city heterogeneity, and healthcare reforms. RESULTS: Healthcare resources supply in the YRD region increases significantly and converges rapidly. There is a significant spatial correlation and agglomeration between provinces and cities, and a significant spatial spillover effect is also found in ß convergence. No evidence is found that the latest healthcare reforms have an impact on the balanced allocation and convergence of healthcare resources. CONCLUSION: China's long-term investment in past decades has yielded a more balanced allocation and intercity convergence of healthcare resources. However, the latest healthcare reforms do not contribute to the balanced allocation of healthcare resources from the supply-side, and demand-side analysis is needed in the future studies.

Hybrid approaches for container traffic forecasting in the context of anomalous events: The case of the Yangtze River Delta region in the COVID-19 pandemic.

The COVID-19 pandemic had a significant impact on container transportation. Accurate forecasting of container throughput is critical for policymakers and port authorities, especially in the context of the anomalous events of the COVID-19 pandemic. In this paper, we firstly proposed hybrid models for univariate time series forecasting to enhance prediction accuracy while eliminating the nonlinearity and multivariate limitations. Next, we compared the forecasting accuracy of different models with various training dataset extensions and forecasting horizons. Finally, we analysed the impact of the COVID-19 pandemic on container throughput forecasting and container transportation. An empirical analysis of container throughputs in the Yangtze River Delta region was performed for illustration and verification purposes. Error metrics analysis suggests that SARIMA-LSTM2 and SARIMA-SVR2 (configuration 2) have the best performance compared to other models and they can better predict the container traffic in the context of anomalous events such as the COVID-19 pandemic. The results also reveal that, with an increase in the training dataset extensions, the accuracy of the models is improved, particularly in comparison with standard statistical models (i.e. SARIMA model). An accurate prediction can help strategic management and policymakers to better respond to the negative impact of the COVID-19 pandemic.

The interaction between black carbon and planetary boundary layer in the Yangtze River Delta from 2015 to 2020: Why O3 didn't decline so significantly as PM2.5.

Since the Air Pollution Prevention and Control Action Plan (air clean plan) issued in 2013, air quality has been in continuous improvement. The second stage of air clean plan since 2018 was focused on O3 controlling, but it still didn't decline so significantly as PM2.5. This study conducted a long-term observation on black carbon (BC) and utilized the observational data of other air pollutants (PM2.5, PM10, NO2, SO2, CO and O3), the meteorological elements and the vertical sounding data of PBL in Nanjing. In the daytime (08:00-20:00), PM2.5 kept decreasing from 2015 to 2020 at the rate of 4.8 µg⋅m-3⋅a-1, however, BC increased at the rate of 0.6 µg⋅m-3⋅a-1, which has led to the continuous growth of BC/PM2.5 (0.9%⋅a-1). However, during this period, O3 was relatively stable and, in 2020, it returned below its value in 2015 after slight increases in 2017 and 2018. Meanwhile, the average surface temperature had increased by around 1.0 °C during 2015-2019 at the rate of 0.3 °C⋅a-1. Also, the average height of the inversion layer had increased significantly by 494.0 and 176.7 m at 20:00 and 08:00, whose growth ratio was up to 57% and 25%, respectively. The above observation results have formed a set of chain reactions as follows. The growth of the surface BC caused the surface temperature to rise due to the increasing heating effect of BC. The continuous growth of the surface temperature made it easier for the PBL height to develop, which led to the lift of the inversion layer in the PBL and the larger atmospheric environment capacity. Ultimately, it is conducive to the diffusion of the near surface pollutants, thus helping reduce their concentrations, which offsets the increasing tendency of O3 and add to the decreasing trend of PM2.5. This phenomenon is the most remarkable in summer, with the fastest increasing rate of temperature (0.8 °C⋅a-1) and O3 (3.9 µg⋅m-3⋅a-1) during 2015-2019 (excluding 2020 to erase the great effect of COVID-19 lockdown on emissions).

Development of the TOA-Related Models for PM2.5 Prediction Pre- and Post-COVID-19 Outbreak over Yangtze River Delta Region of China

The lockdown and the strict regulation measures implemented by Chinese government due to the outbreak of the COVID-19 pandemic not only decelerated the spread of the virus but also brought a positive effect on the nationwide atmospheric quality. In this study, we extended our previous research on remotely sensed estimation of PM2.5 concentrations in Yangtze River Delta region (i.e., YRD) of China from 2019 to the strict regulation period of 2020 (i.e., 24 Jan, 2020-31 Aug, 2020). Unlike the method using aerosol optical depth (AOD) developed in previous studies, we validated the possibility of moderate resolution imaging spectroradiometer (MODIS) top-of-atmosphere (TOA) reflectance (i.e., MODIS TOA) at 21 bands in estimating the PM2.5 concentrations in YRD region. Two random forests (i.e., TOA-sig RF and TOA-all RF) incorporated with different MODIS TOA datasets were developed, and the results showed that the TOA-sig RF model performed better with R2 of 0.81 (RMSE=8.07 μg/m3) than TOA-all RF model with R2 of 0.79 (RMSE=9.13 μg/m3). The monthly averaged PM2.5 exhibited the highest value of 50.81 μg/m3 in YRD region in January 2020 and sharply decreased from February to August 2020. The annual mean PM2.5 concentrations derived by TOA-sig RF model were 47.74, 32.14, and 21.04 μg/m3 in winter, spring, and summer in YRD during the strict regulation period of 2020, respectively, showing much lower values than those in 2019. Our research demonstrated that the PM2.5 concentrations could be effectively estimated by using MODIS TOA reflectance at 21 bands and the random forest.

Evaluation of Long-Term Modeling Fine Particulate Matter and Ozone in China During 2013-2019

Air quality in China has been undergoing significant changes due to the implementation of extensive emission control measures since 2013. Many observational and modeling studies investigated the formation mechanisms of fine particulate matter (PM2.5) and ozone (O-3) pollution in the major regions of China. To improve understanding of the driving forces for the changes in PM2.5 and O-3 in China, a nationwide air quality modeling study was conducted from 2013 to 2019 using the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) modeling system. In this study, the model predictions were evaluated using the observation data for the key pollutants including O-3, sulfur dioxide (SO2), nitrogen dioxide (NO2), and PM2.5 and its major components. The evaluation mainly focused on five major regions, that is , the North China Plain (NCP), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), the Chengyu Basin (CY), and the Fenwei Plain (FW). The CMAQ model successfully reproduced the air pollutants in all the regions with model performance indices meeting the suggested benchmarks. However, over-prediction of PM2.5 was noted in CY. NO2, O-3,O- and PM2.5 were well simulated in the north compared to the south. Nitrate (NO3-) and ammonium (NH4+) were the most important PM2.5 components in heavily polluted regions. For the performance on different pollution levels, the model generally over-predicted the clean days but underpredicted the polluted days. O-3 was found increasing each year, while other pollutants gradually reduced during 2013-2019 across the five regions. In all of the regions except PRD (all seasons) and YRD (spring and summer), the correlations between PM2.5 and O-3 were negative during all four seasons. Low-to-medium correlations were noted between the simulated PM2.5 and NO2, while strong and positive correlations were established between PM2.5 and SO2 during all four seasons across the five regions. This study validates the ability of the CMAQ model in simulating air pollution in China over a long period and provides insights for designing effective emission control strategies across China.

Structural Differences of PM2.5 Spatial Correlation Networks in Ten Metropolitan Areas of China

The cross-impact of environmental pollution among cities has been reported in more research works recently. To implement the coordinated control of environmental pollution, it is necessary to explore the structural characteristics and influencing factors of the PM2.5 spatial correlation network from the perspective of the metropolitan area. This paper utilized the gravity model to construct the PM2.5 spatial correlation network of ten metropolitan areas in China from 2019 to 2020. After analyzing the overall characteristics and node characteristics of each spatial correlation network based on the social network analysis (SNA) method, the quadratic assignment procedure (QAP) regression analysis method was used to explore the influence mechanism of each driving factor. Patent granted differences, as a new indicator, were also considered during the above. The results showed that: (1) In the overall network characteristics, the network density of Chengdu and the other three metropolitan areas displayed a downward trend in two years, and the network density of Wuhan and Chengdu was the lowest. The network density and network grade of Hangzhou and the other four metropolitan areas were high and stable, and the network structure of each metropolitan area was unstable. (2) From the perspective of the node characteristics, the PM2.5 spatial correlation network all performed trends of centralization and marginalization. Beijing-Tianjin-Hebei and South Central Liaoning were “multi-core” metropolitan areas, and the other eight were “single-core” metropolitan areas. (3) The analysis results of QAP regression illustrated that the top three influencing factors of the six metropolitan areas were geographical locational relationship, the secondary industrial proportion differences, respectively, and patent granted differences, and the other metropolitan areas had no dominant influencing factors.

Insights into the significant increase in ozone during COVID-19 in a typical urban city of China

The outbreak of COVID-19 promoted strict restrictions to human activities in China, which led to a dramatic decrease in most air pollutant concentrations (e.g., PM2.5, PM10, NOx, SO2 and CO). However, an obvious increase in ozone (O3) concentrations was found during the lockdown period in most urban areas of China. In this study, we conducted field measurements targeting ozone and its key precursors by utilizing a novel proton transfer reaction time-of-flight mass spectrometer (PTR-TOF-MS) in Changzhou, which is representative of the Yangtze River Delta (YRD) city cluster of China. We further applied the integrated methodology including machine learning, an observation-based model (OBM) and sensitivity analysis to obtain insights into the reasons causing the obvious increase in ozone. Major findings include the following: (1) by deweathered calculation, we found changes in precursor emissions contributed 1.46 ppbv to the increase in the observed O3 during the full-lockdown period in 2020, while meteorology constrained 3.0 ppbv of O3 in the full-lockdown period of 2019. (2) By using an OBM, we found that although a significant reduction in O3 precursors was observed during the full-lockdown period, the photochemical formation of O3 was stronger than that during the pre-lockdown period. (3) The NOx/VOC ratio dropped dramatically from 1.84 during the pre-lockdown to 0.79 in the full-lockdown period, which switched O3 formation from a VOC-limited regime to the boundary of a NOx- and VOC-limited regime. Additionally, box model results suggested that the decrease in the NOx/VOC ratio during the full-lockdown period could increase the mean O3 by 2.4 ppbv. Results of this study give insights into the relationship between O3 and its precursors in urban area and demonstrate reasons for the obvious increase in O3 in most urban areas of China during the COVID-19 lockdown period. This study also underlines the necessity of controlling anthropogenic oxygenated volatile organic compounds (OVOCs), alkenes and aromatics in the sustained campaign of reducing O3 pollution in China.

Spatio-Temporal Characteristics of Air Quality Index (AQI) over Northwest China

In recent years, air pollution has become a serious threat, causing adverse health effects and millions of premature deaths in China. This study examines the spatial-temporal characteristics of ambient air quality in five provinces (Shaanxi (SN), Xinjiang (XJ), Gansu (GS), Ningxia (NX), and Qinghai (QH)) of northwest China (NWC) from January 2015 to December 2018. For this purpose, surface-level aerosol pollutants, including particulate matter (PMx, x = 2.5 and 10) and gaseous pollutants (sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O-3)) were obtained from China National Environmental Monitoring Center (CNEMC). The results showed that fine particulate matter (PM2.5), coarse particulate matter (PM10), SO2, NO2, and CO decreased by 28.2%, 32.7%, 41.9%, 6.2%, and 27.3%, respectively, while O-3 increased by 3.96% in NWC during 2018 as compared with 2015. The particulate matter (PM2.5 and PM10) levels exceeded the Chinese Ambient Air Quality Standards (CAAQS) Grade II standards as well as the WHO recommended Air Quality Guidelines, while SO2 and NO2 complied with the CAAQS Grade II standards in NWC. In addition, the average air quality index (AQI), calculated from ground-based data, improved by 21.3%, the proportion of air quality Class I (0-50) improved by 114.1%, and the number of pollution days decreased by 61.8% in NWC. All the pollutants' (except ozone) AQI and PM2.5/PM10 ratios showed the highest pollution levels in winter and lowest in summer. AQI was strongly positively correlated with PM2.5, PM10, SO2, NO2, and CO, while negatively correlated with O-3. PM10 was the primary pollutant, followed by O-3, PM2.5, NO2, CO, and SO2, with different spatial and temporal variations. The proportion of days with PM2.5, PM10, SO2, and CO as the primary pollutants decreased but increased for NO2 and O-3. This study provides useful information and a valuable reference for future research on air quality in northwest China.

High atmospheric oxidation capacity drives wintertime nitrate pollution in the eastern Yangtze River Delta of China

Nitrate aerosol plays an increasingly important role in wintertime haze pollution in China. Despite intensive research on wintertime nitrate chemistry in recent years, quantitative constraints on the formation mechanisms of nitrate aerosol in the Yangtze River Delta (YRD), one of the most developed and densely populated regions in eastern China, remain inadequate. In this study, we identify the major nitrate formation pathways and their key controlling factors during the winter haze pollution period in the eastern YRD using 2-year (2018–2019) field observations and detailed observation-constrained model simulations. We find that the high atmospheric oxidation capacity, coupled with high aerosol liquid water content (ALWC), made both the heterogeneous hydrolysis of dinitrogen pentoxide (N2O5) and the gas-phase OH oxidation of nitrogen dioxide (NO2) important pathways for wintertime nitrate formation in this region, with contribution percentages of 69 % and 29 % in urban areas and 63 % and 35 % in suburban areas during the haze pollution episodes, respectively. We further find that the gas-to-particle partitioning of nitric acid (HNO3) was very efficient so that the rate-determining step in the overall formation process of nitrate aerosol was the oxidation of NOx to HNO3 through both heterogeneous and gas-phase processes. The atmospheric oxidation capacity (i.e., the availability of O3 and OH radicals) was the key factor controlling the production rate of HNO3 from both processes. During the COVID-19 lockdown (January–February 2020), the enhanced atmospheric oxidation capacity greatly promoted the oxidation of NOx to nitrate and hence weakened the response of nitrate aerosol to the emission reductions in urban areas. Our study sheds light on the detailed formation mechanisms of wintertime nitrate aerosol in the eastern YRD and highlights the demand for the synergetic regulation of atmospheric oxidation capacity and NOx emissions to mitigate wintertime nitrate and haze pollution in eastern China.