Spatial and temporal variations of air quality and six air pollutants in China during 2015-2017

Estimating four-decadal variations of seagrass distribution using satellite data and deep learning methods in a marine lagoon

Seagrasses are marine flowering plants that inhabit shallow coastal and estuarine waters and serve vital ecological functions in marine ecosystems. However, seagrass ecosystems face the looming threat of degradation, necessitating effective monitoring. Remote-sensing technology offers significant advantages in terms of spatial coverage and temporal accessibility. Although some remote sensing approaches, such as water column correction, spectral index-based, and machine learning-based methods, have been proposed for seagrass detection, their performances are not always satisfactory. Deep learning models, known for their powerful learning and vast data processing capabilities, have been widely employed in automatic target detection. In this study, a typical seagrass habitat (Swan Lake) in northern China was used to propose a deep learning-based model for seagrass detection from Landsat satellite data. The performances of UNet and SegNet at different patch scales for seagrass detection were compared. The results showed that the SegNet model at a patch scale of 16 × 16 pixels worked well, with validation accuracy and loss of 96.3 % and 0.15, respectively, during training. Evaluations based on the test dataset also indicated good performance of this model, with an overall accuracy >95 %. Subsequently, the deep learning model was applied for seagrass detection in Swan Lake between 1984 and 2022. We observed a noticeable seasonal variation in germination, growth, maturation, and shrinkage from spring to winter. The seagrass area decreased over the past four decades, punctuated by intermittent fluctuations likely attributed to anthropogenic activities, such as aquaculture and construction development. Additionally, changes in landscape ecology indicators have demonstrated that seagrass experiences severe patchiness. However, these problems have weakened recently. Overall, by combining remote sensing big data with deep learning technology, our study provides a valuable approach for the highly precise monitoring of seagrass. These findings on seagrass area variation in Swan Lake offer significant information for seagrass restoration and management.

Association of short-term nitrogen dioxide exposure with hospitalization for urolithiasis in Xinxiang, China: a time series study

Urolithiasis accounts for the highest incidence of all urologic-associated hospitalizations. However, few studies have explored the effect of nitrogen dioxide (NO2) on hospitalizations for urolithiasis. We included 5956 patients with urolithiasis, collected daily meteorological and air pollution data between 2016 and 2021, and analyzed the associations between air pollutants and hospitalization, length of the hospital stay, and hospitalization costs attributable to urolithiasis. NO2 exposure was associated with an increased risk of hospitalization for urinary tract stones. For each 10-μg/m3 increase and 1-day lag of NO2, the maximum daily effect on the risk of hospitalization for urolithiasis was 1.020 (95% confidence interval [CI]: 1.001-1.039), and the cumulative effect peaked on lag day 4 (relative risk [RR]: 1.061; 95% CI: 1.003-1.122). Attribution scores and quantitative analysis revealed that the mean number of hospital days and mean hospital costs were 16 days and 21,164.39 RMB, respectively. Up to 5.75% of all urolithiasis hospitalizations were estimated to be attributable to NO2, and the cost of NO2-related urolithiasis hospitalizations reached approximately 3,430,000 RMB. Stratified analysis showed that NO2 had a more sensitive impact on urolithiasis hospitalizations in women and in those aged ≥65 years. Notably, men and those younger than 65 years of age (exclude people aged 65) incurred more costs for urolithiasis hospitalizations. In the population level, the association between NO2 and risk of urolithiasis hospitalization was more pronounced during the warm season. NO2 can increase hospitalizations for urolithiasis for Xinxiang City residents, and there is a cumulative lag effect. Focusing on air pollution may have practical significance in terms of the prevention and control of urolithiasis.

A critical review and prospect of NO2 and SO2 pollution over Asia: Hotspots, trends, and sources

Nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) are two major atmospheric pollutants that significantly threaten human health, the environment, and ecosystems worldwide. Despite this, only some studies have investigated the spatiotemporal hotspots of NO₂ and SO₂, their trends, production, and sources in Asia. Our study presents a literature review covering the production, trends, and sources of NO₂ and SO₂ across Asian countries (e.g., Bangladesh, China, India, Iran, Japan, Pakistan, Malaysia, Kuwait, and Nepal). Based on the findings of the review, NO₂ and SO₂ pollution are increasing due to industrial activity, fossil fuel burning, biomass burning, heavy traffic movement, electricity generation, and power plants. There is significant concern about health risks associated with NO₂ and SO₂ emissions in Bangladesh, China, India, Malaysia, and Iran, as they pay less attention to managing and controlling pollution. Even though the lack of quality datasets and adequate research in most Asian countries further complicates the management and control of NO₂ and SO₂ pollution. This study has NO₂ and SO₂ pollution scenarios, including hotspots, trends, sources, and their influences on Asian countries. This study highlights the existing research gaps and recommends new research on identifying integrated sources, their variations, spatiotemporal trends, emission characteristics, and pollution level. Finally, the present study suggests a framework for controlling and monitoring these two pollutants' emissions.

Air pollution in heavy industrial cities along the northern slope of the Tianshan Mountains, Xinjiang: characteristics, meteorological influence, and sources

The spatiotemporal characteristics, relationship with meteorological factors, and source distribution of air pollutants (January 2017-December 2021) were analyzed to better understand the air pollutants on the northern slope of the Tianshan Mountains (NSTM) in Xinjiang, a heavily polluted urban agglomeration of heavy industries. The results showed that the annual mean concentrations of SO₂, NO₂, CO, O₃, PM_2.5, and PM₁₀ were 8.61-13.76 μg m^-3, 26.53-36.06 μg m^-3, 0.79-1.31 mg m^-3, 82.24-87.62 μg m^-3, 37.98-51.10 μg m^-3, and 84.15-97.47 μg m^-3. The concentrations of air pollutants (except O₃) showed a decreasing trend. The highest concentrations were in winter, and in Wujiaqu, Shihezi, Changji, Urumqi, and Turpan, the concentrations of particulate matter exceeded the NAAQS Grade II during winter. The west wind and the spread of local pollutants both substantially impacted the high concentrations. According to the analysis of the backward trajectory in winter, the air masses were mainly from eastern Kazakhstan and local emission sources, and PM₁₀ in the airflow had a more significant impact on Turpan; the rest of the cities were more affected by PM_2.5. Potential sources included Urumqi-Changj-Shihezi, Turpan, the northern Bayingol Mongolian Autonomous Prefecture, and eastern Kazakhstan. Consequently, the emphasis on improving air quality should be on reducing local emissions, strengthening regional cooperation, and researching transboundary transport of air pollutants.

Change in Air Quality during 2014-2021 in Jinan City in China and Its Influencing Factors

Air pollution affects climate change, food production, traffic safety, and human health. In this paper, we analyze the changes in air quality index (AQI) and concentrations of six air pollutants in Jinan during 2014-2021. The results indicate that the annual average concentrations of PM₁₀, PM_2.5, NO₂, SO₂, CO, and O₃ and AQI values all declined year after year during 2014-2021. Compared with 2014, AQI in Jinan City fell by 27.3% in 2021. Air quality in the four seasons of 2021 was obviously better than that in 2014. PM_2.5 concentration was the highest in winter and PM_2.5 concentration was the lowest in summer, while it was the opposite for O₃ concentration. AQI in Jinan during the COVID epoch in 2020 was remarkably lower compared with that during the same epoch in 2021. Nevertheless, air quality during the post-COVID epoch in 2020 conspicuously deteriorated compared with that in 2021. Socioeconomic elements were the main reasons for the changes in air quality. AQI in Jinan was majorly influenced by energy consumption per 10,000-yuan GDP (ECPGDP), SO₂ emissions (SDE), NOx emissions (NOE), particulate emissions (PE), PM_2.5, and PM₁₀. Clean policies in Jinan City played a key role in improving air quality. Unfavorable meteorological conditions led to heavy pollution weather in the winter. These results could provide a scientific reference for the control of air pollution in Jinan City.

Understanding and revealing the intrinsic impacts of the COVID-19 lockdown on air quality and public health in North China using machine learning

To avoid the spread of COVID-19, China implemented strict prevention and control measures, resulting in dramatic variations in urban and regional air quality. With the complex effect from long-term emission mitigation and meteorology variation, an accurate evaluation of the net effect from lockdown on air quality changes has not been fully quantified. Here, we combined machine learning algorithm and Theil-Sen regression technique to eliminate meteorological and long-term trends effects on air pollutant concentrations and precisely detect concentrations changes those ascribed to lockdown measures in North China. Our results showed that, compared to the same period in 2015-2019, the adverse meteorology during the lockdown period (January 25th to March 15th) in early 2020 increased PM_2.5 concentration in North China by 9.8 %, while the reduction of anthropogenic emissions led to a 32.2 % drop. Stagnant meteorological conditions have a more significant impact on the ground-level air quality in the Beijing-Tianjin-Hebei Region than that in Shanxi and Shandong provinces. After further striping out the effect of long-term emission reduction trend, the lockdown-derived NO₂, PM_2.5, and O₃ shown variety change trend, and at -30.8 %, -27.6 %, and +10.0 %, respectively. Air pollutant changes during the lockdown could be overestimated up to 2-fold without accounting for the influences of meteorology and long-term trends. Further, with pollution reduction during the lockdown period, it would avoid 15,807 premature deaths in 40 cities. If with no deteriorate meteorological condition, the total avoided premature should increase by 1146.

The diminishing effects of winter heating on air quality in northern China

Cleaner winter heating has been promoted to abate the winter air pollution in northern China. Although improvements in air quality have been observed, the effectiveness and mechanism of cleaner heating measures on air quality have not been examined on the empirical ground. In this study, we estimate the annual effects of winter heating policy on air quality from 2014 to 2017 using a regression discontinuity design (RDD) and dynamic regression model. The results show that winter heating aggravates Air Quality Index (AQI). Specifically, the AQI raised by winter heating reduce from 85.3 in 2014 to 24.1 in 2017, indicating diminishing effects of winter heating with the implementation of clean heating measures. The heterogeneous characteristics of winter heating in terms of different pollutants and city scales are further quantified. The effects of clean heating are more evident for particulate pollutants (PM_2.5 and PM₁₀) than for SO₂, NO₂, CO and O₃. The promotion of clean heating is more effective in larger cities. These findings provided insights into the diminishing air pollution change with continuous advancement in clean heating policy and the heterogeneity among cities and pollutants should be taken into account when formulating future policies in response to energy transition and climate change.

Spatial Autocorrelation and Temporal Convergence of PM2.5 Concentrations in Chinese Cities

Scientific study of the temporal and spatial distribution characteristics of haze is important for the governance of haze pollution and the formulation of environmental policies. This study used panel data of the concentrations of particulate matter sized < 2.5 μm (PM_2.5) in 340 major cities from 1999 to 2016 to calculate the spatial distribution correlation by the spatial analysis method and test the temporal convergence of the urban PM_2.5 concentration distribution using an econometric model. It found that the spatial autocorrelation of PM_2.5 seemed positive, and this trend increased over time. The yearly concentrations of PM_2.5 were converged, and the temporal convergence fluctuated under the influence of specific historical events and economic backgrounds. The spatial agglomeration effect of PM_2.5 concentrations in adjacent areas weakened the temporal convergence of PM_2.5 concentrations. This paper introduced policy implications for haze prevention and control.

Air Pollution and Migration Intention: Evidence from the Unified National Graduate Entrance Examination

Using a unique dataset of applicants for the Unified National Graduate Entrance Examination (UNGEE) of 76 double first-class universities in China, this paper evaluates the causal impact of air pollution on the migration intentions of highly educated talents by exploiting an instrumental variable approach based on annually average wind speed. We find that a 1 ug/m3 increase in the annually average PM2.5 concentration in destination cities decreases the number of applicants for the UNGEE of elite universities by about 250, but better university quality and more abundant educational resources can weaken the effect partially. A heterogeneity analysis indicates that the university-city choices of applicants are shifting from north to south. Our findings suggest that air pollution may lead to the loss of high human capital.

The regional impact of the COVID-19 lockdown on the air quality in Ji'nan, China

A number of strict lockdown measures were implemented in the areas most affected by COVID-19 in China, including Ji'nan city, from 24 January to 7 February 2020. Due to these forced restrictions, the pollution levels in cities across the country drastically decreased within just a few days. Since traffic pollution and industrial emissions are important factors affecting regional air quality, congestion has a significant impact on the environment. Therefore, using the aid of air quality data for six pollutants (PM10, PM2.5, SO2, NO2, CO and O3) from 11 monitoring stations (located in urban, suburban and urban-industrial regions) across Ji'nan, we employed the air quality index (AQI) to investigate the spatial pattern of air quality in the pre-COVID-19 (pre-COVID) and COVID-19-related lockdown (COVID lockdown) periods. The results showed that air quality significantly improved during the COVID lockdown period. Among the selected pollutants, compared to the corresponding pre-COVID levels, the greatest reduction was observed for the concentration of NO2 (54.02%), while the smallest reduction was observed for the concentration of SO2 (27.92%). The PM2.5 (38.73%), PM10 (44.92%) and CO (30.60%) levels also decreased during the COVID lockdown period; only the O3 concentration increased (37.42%) during this period. Overall, air quality improved by approximate improvements of 37.33% during the COVID lockdown period. Approximately 35.48%, 37.01% and 43.43% in the AQI were observed in urban, suburban and urban-industrial regions, respectively. Therefore, the AQI exhibited remarkable regional differences in Ji'nan. This study demonstrates the contributions of the transportation sector and local emissions to improving air quality in typical urban areas, and these research results can provide guidance for the further monitoring of air pollution in northern Chinese cities.

Restricted Anthropogenic Activities and Improved Urban Air Quality in China: Evidence from Real-Time and Remotely Sensed Datasets Using Air Quality Zonal Modeling

The study aims to examine the major atmospheric air pollutants such as NO2, CO, O3, PM2.5, PM10, and SO2 to assess the overall air quality using air quality zonal modeling of 15 major cities of China before and after the COVID-19 pandemic period. The spatio-temporal changes in NO2 and other atmospheric pollutants exhibited enormous reduction due to the imposition of a nationwide lockdown. The present study used a 10-day as well as 60-day tropospheric column time-average map of NO2 with spatial resolution 0.25 × 0.25° obtained from the Global Modeling and Assimilation Office, NASA. The air quality zonal model was employed to assess the total NO2 load and its change during the pandemic period for each specific region. Ground surface monitoring data for CO, NO2, O3, PM10, PM2.5, and SO2 including Air Quality Index (AQI) were collected from the Ministry of Environmental Protection of China (MEPC). The results from both datasets demonstrated that NO2 has drastically dropped in all the major cities across China. The concentration of CO, PM10, PM2.5, and SO2 demonstrated a decreasing trend whereas the concentration of O3 increased substantially in all cities after the lockdown effect as observed from real-time monitoring data. Because of the complete shutdown of all industrial activities and vehicular movements, the atmosphere experienced a lower concentration of major pollutants that improves the overall air quality. The regulation of anthropogenic activities due to the COVID-19 pandemic has not only contained the spread of the virus but also facilitated the improvement of the overall air quality. Guangzhou (43%), Harbin (42%), Jinan (33%), and Chengdu (32%) have experienced maximum air quality improving rates, whereas Anshan (7%), Lanzhou (17%), and Xian (25%) exhibited less improved AQI among 15 cities of China during the study period. The government needs to establish an environmental policy framework involving central, provincial, and local governments with stringent laws for environmental protection.

Effects of energy structure differences on chemical compositions and respiratory health of PM2.5 during late autumn and winter in China

To understand the influence of the energy structure (including solid fuel and clean energy) on air pollution, two comprehensive measurement campaigns were conducted in Baoding and Shanghai in late autumn and winter during 2017-2018. The chemical compositions, driving factors, regional transport of pollutants, and potential respiratory disease (RD) health risks of PM_2.5 for Baoding and Shanghai were analyzed. The results showed that the concentration of PM_2.5 in Baoding (156.9 ± 139.8 μg m^-3) was 2.6 times of that in Shanghai (60.9 ± 45.9 μg m^-3). The most important contributor to PM_2.5 in Baoding was organic matter (OM), while inorganic aerosols accounted for major fractions of PM_2.5 in Shanghai. Positive matrix factorization (PMF) results indicated that coal combustion (CC; 39%) accounted for the most in Baoding, followed by secondary aerosols (21%), biomass burning (BB; 20%), industrial emissions (14%), dust (3%), and vehicle exhaust (2%). However, the average contribution in Shanghai followed the order: secondary aerosols (44%), vehicle exhaust (36%), dust (11%), marine aerosols (6%), and BB (3%). The evolution of source contributions at different pollution levels revealed that haze episodes in Baoding and Shanghai were triggered by CC and secondary formation, respectively; however, the air quality on clean days in Baoding and Shanghai was affected mostly by BB and vehicle emissions, respectively. Potential source contribution function (PSCF) results suggested that CC in Baoding was primarily from local emissions, while BB was primarily from local and regional transport. Vehicle exhaust and secondary aerosols in Shanghai were mainly from local emissions and regional transport. The number of RD deaths related to haze episodes in Baoding and Shanghai were 215 (95% CI: 109, 319) and 76 (95% CI: 11, 135), respectively. This research also emphasized the importance of further attention to the usage of coal in Baoding and vehicle emissions in Shanghai.

The Impact of the Numbers of Monitoring Stations on the National and Regional Air Quality Assessment in China During 2013-18

China national air quality monitoring network has become the core data source for air quality assessment and management in China. However, during network construction, the significant change in numbers of monitoring sites with time is easily ignored, which brings uncertainty to air quality assessments. This study aims to analyze the impact of change in numbers of stations on national and regional air quality assessments in China during 2013-18. The results indicate that the change in numbers of stations has different impacts on fine particulate matter (PM2.5) and ozone concentration assessments. The increasing number of sites makes the estimated national and regional PM2.5 concentration slightly lower by 0.6-2.2 µg m-3 and 1.4-6.0 µg m-3 respectively from 2013 to 2018. The main reason is that over time, the monitoring network expands from the urban centers to the suburban areas with low population densities and pollutant emissions. For ozone, the increasing number of stations affects the long-term trends of the estimated concentration, especially the national trends, which changed from a slight upward trend to a downward trend in 2014-15. Besides, the impact of the increasing number of sites on ozone assessment exhibits a seasonal difference at the 0.05 significance level in that the added sites make the estimated concentration higher in winter and lower in summer. These results suggest that the change in numbers of monitoring sites is an important uncertainty factor in national and regional air quality assessments, that needs to be considered in long-term concentration assessment, trend analysis, and trend driving force analysis.

Effect of COVID-19 Response Policy on Air Quality: A Study in South China Context

Mass suspension of anthropogenic activities is extremely rare, the quarantine due to the coronavirus disease 2019 (COVID-19) represents a natural experiment to investigate the impact of anthropogenic activities on air quality. The mitigation of air pollution during the COVID-19 lockdown has been reported from a global perspective; however, the air pollution levels vary in different regions. This study initiated a novel synthesis of multiple-year satellite observations, national ground measurements towards SO2, NO2 and O3 and meteorological conditions to evaluate the impact of the COVID-19 lockdown in Beihai, a specific city in a less developed area in southwest China, to reveal the potential implications of control strategies for air pollution. The levels of the major air pollutants during the COVID-19 lockdown (LP) and during the same period of previous years (SP) were compared and a series of statistical tools were applied to analyze the sources of air pollution in Beihai. The results show that air pollutant levels decreased with substantial diversity during the LP. Satellite-retrieved NO2 and SO2 levels during the LP decreased by 5.26% and 22.06%, while NO2, SO2, PM2.5 and PM10 from ground measurements during the LP were 25.6%, 2.7%, 22.2% and 22.2% lower than during SP, respectively. Ground measured SO2 concentrations during the LP were only 2.7% lower than during the SP, which may be attributed to uninterrupted essential industrial activities, such as power plants. Polar plots analysis shows that NO2 concentrations were strongly associated with local emission sources, such as automobiles and local industry. Additionally, the much lower levels of NO2 concentrations during the LP and the absence of an evening peak may highlight the significant impact of the traffic sector on NO2. The decrease in daily mean O3 concentrations during the LP may be associated with the reduction in NO2 concentrations. Indications in this study could be beneficial for the formulation of atmospheric protection policies.

Sex-difference in air pollution-related acute circulatory and respiratory mortality and hospitalization

BACKGROUND

Numerous studies have estimated adverse effects of short-term exposure to ambient air pollution on public health. Few have focused on sex-differences, and results have been inconsistent. The purpose of this study was three-fold: to identify sex-differences in air pollution-related health outcomes; to examine sex-differences by cause and season; and to examine time trends in sex-differences.

METHODS

Daily data were collected on circulatory- and respiratory-related mortality (for 29 years) and cause-specific hospitalization (for 17 years) with hourly concentrations of ozone (O3), nitrogen dioxide (NO2), and fine particulate matter (PM2.5). For hospitalization, more specific causes were examined: ischemic heart disease (IHD), other heart disease (OHD), cerebrovascular disease (CEV), chronic lower respiratory diseases (CLRD), and Influenza/Pneumonia (InfPn). Generalized Poisson models were applied to 24 Canadian cities, and the city-specific estimates were combined for nationwide estimates for each sex using Bayesian hierarchical models. Finally, sex-differences were tested statistically based on their interval estimates, considering the correlation between sex-specific national estimates.

RESULTS

Sex-differences were more frequently observed for hospitalization than mortality, respiratory than circulatory health outcomes, and warm than cold season. For hospitalization, males were at higher risk (M > F) for warm season (OHD and InfPn from O3; IHD from NO2; and InfPn from PM2.5), but F > M for cold season (CEV from O3 and OHD from NO2). For mortality, we found F > M only for circulatory diseases from ozone during the warm season. Among the above-mentioned sex-differences, three cases showed consistent time trends over the years: while M > F for OHD from O3 and IHD from NO2, F > M for OHD from NO2.

CONCLUSIONS

We found that sex-differences in effect of ambient air pollution varied over health outcome, cause, season and time. In particular, the consistent trends (either F > M or M > F) across 17 years provide stronger evidence of sex-differences in hospitalizations, and warrant investigation in other populations.

The effect of PM2.5-related hazards on biomarkers of bronchial epithelial cells (A549) inflammation in Karaj and Fardis cities

Fine particles (especially PM2.5 particles) in ambient air can cause irreversible effects on human health. In the present study, seasonal variations in toxicity PM2.5 (cell viability and release of pro-inflammatory cytokines) were exposed human lung cells (A549) to concentrations of PM2.5 samples in summer (sPM2.5) and winter (wPM2.5) seasons. Cells were separately exposed to three concentrations of PM2.5 (25, 50, and 100 μg/mL) and three times (12 h, 1 and 2 days). We evaluated cell viability by MTT assay [3- (4, 5-dimethylthiazol-2-yl) -2, 5-diphenyltetrazolium bromide] and liberation of pro-inflammatory cytokines (interleukin-6 and interleukin-8) by the ELISA method. The toxicological results of this study showed that increasing the concentration of PM2.5 particulates and contact time with it reduces cell viability and increases inflammatory responses. Seasonal cytotoxicity of PM2.5 particles in high-traffic areas at summer season compared to winter season was lower. The lowest percent of viability at 2 days of exposure and 100 μg/mL exposure in the winter sample was observed. Also, PM2.5 particles were influential in the amount of interleukins 8 and 6. The average release level of IL-6 and IL-8 in the cold season (winter) and the enormous exposure time and concentrations (2 days-100 μg/mL) was much higher than in the hot season (summer). These values were twice as high for winter PM2.5 samples as for summer samples. The compounds in PM2.5 at different seasons can cause some biological effects. The samples' chemical characteristics in two seasons displayed that the PMs were diverse in chemical properties. In general, heavy metals and polycyclic aromatic hydrocarbons were more in the winter samples. However, the samples of wPM2.5 had a lower mass quota of metals such as aluminum, iron, copper, zinc, and magnesium. Concentrations of chromium, cadmium, arsenic, mercury, nickel, and lead were more significant in the sample of wPM2.5.

Air quality during three covid-19 lockdown phases: AQI, PM2.5 and NO2 assessment in cities with more than 1 million inhabitants

Implemented quarantine due to the ongoing novel coronavirus (agent of COVID-19) has an immense impact on human mobility and economic activities as well as on air quality. Since then, and due to the drastic reduction in pollution levels in cities across the world, a large discussion has been magnetized regarding if the lockdown is an adequate alternative counter-measure for enhancing air quality. This paper aimed at studying the Air Quality Index (AQI), PM2.5, and tropospheric NO2 levels in three lockdown phases (before, during, and after) among 21 cities around the world. Simple before/after comparison approach was carried out to capture the declining trend in air pollution levels caused by the lockdown restrictions. The results showed that the frequency distribution for NO2 is more variable than that for PM2.5, and the distribution is flatter from 2020 to the baseline 2018-2019 period. Besides, AQI, in most of the cities, has varied from high to mild pollution during the lockdown and was moderate before. Although during the lockdown, a reduction of 3 to 58% of daily NO2 concentrations was observed across the cities, an increase was detected in three cities including Abidjan (1%), Conakry (3%), and Chengdu (10%). Despite this mixed trend, the NO2 time series clearly showed the effect of the unlocking phase where the NO2 levels increased in almost all cities. Similarly, PM2.5 concentrations have increased in the post-lockdown period, with 50% of the cities reporting significant positive differences between the lock and the unlock phase. Then, the levels of PM2.5 were higher at the pre-lockdown phase than at any other time exhibiting a "U" shape. In addition, during Ramadan, it was noted that altered patterns of daily activities in some Islamic cities have a significant negative impact on air quality.

Facile Fabrication of Hybrid Carbon Nanotube Sensors by Laser Direct Transfer

Ammonia is one of the most frequently produced chemicals in the world, and thus, reliable measurements of different NH₃ concentrations are critical for a variety of industries, among which are the agricultural and healthcare sectors. The currently available technologies for the detection of NH₃ provide accurate identification; however, they are limited by size, portability, and fabrication cost. Therefore, in this work, we report the laser-induced forward transfer (LIFT) of single-walled carbon nanotubes (SWCNTs) decorated with tin oxide nanoparticles (SnO₂ NPs), which act as sensitive materials in chemiresistive NH₃ sensors. We demonstrate that the LIFT-fabricated sensors can detect NH₃ at room temperature and have a response time of 13 s (for 25 ppm NH₃). In addition, the laser-fabricated sensors are fully reversible when exposed to multiple cycles of NH₃ and have an excellent theoretical limit of detection of 24 ppt.

Identification of NO2 and SO2 Pollution Hotspots and Sources in Jiangsu Province of China

Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are important atmospheric trace gases for determining air quality, human health, climate change, and ecological conditions both regionally and globally. In this study, the Ozone Monitoring Instrument (OMI), total column nitrogen dioxide (NO2), and sulfur dioxide (SO2) were used from 2005 to 2020 to identify pollution hotspots and potential source areas responsible for air pollution in Jiangsu Province. The study investigated the spatiotemporal distribution and variability of NO2 and SO2, the SO2/NO2 ratio, and their trends, and potential source contribution function (PSCF) analysis was performed to identify potential source areas. The spatial distributions showed higher values (>0.60 DU) of annual mean NO2 and SO2 for most cities of Jiangsu Province except for Yancheng City (<0.50 DU). The seasonal analyses showed the highest NO2 and SO2 in winter, followed by spring, autumn, and summer. Coal-fire-based room heating and stable meteorological conditions during the cold season may cause higher NO2 and SO2 in winter. Notably, the occurrence frequency of NO2 and SO2 of >1.2 was highest in winter, which varied between 9.14~32.46% for NO2 and 7.84~21.67% for SO2, indicating a high level of pollution across Jiangsu Province. The high SO2/NO2 ratio (>0.60) indicated that industry is the dominant source, with significant annual and seasonal variations. Trends in NO2 and SO2 were calculated for 2005–2020, 2006–2010 (when China introduced strict air pollution control policies during the 11th Five Year Plan (FYP)), 2011–2015 (during the 12th FYP), and 2013–2017 (the Action Plan of Air Pollution Prevention and Control (APPC-AC)). Annually, decreasing trends in NO2 were more prominent during the 12th FYP period (2011–2015: −0.024~−0.052 DU/year) than in the APPC-AC period (2013–2017: −0.007~−0.043 DU/year) and 2005–2020 (−0.002 to −0.012 DU/year). However, no prevention and control policies for NO2 were included during the 11th FYP period (2006–2010), resulting in an increasing trend in NO2 (0.015 to 0.031) observed throughout the study area. Furthermore, the implementation of China’s strict air pollution control policies caused a larger decrease in SO2 (per year) during the 12th FYP period (−0.002~−0.075 DU/year) than in the 11th FYP period (−0.014~−0.071 DU/year), the APPC-AC period (−0.007~−0.043 DU/year), and 2005–2020 (−0.015~−0.032 DU/year). PSCF analysis indicated that the air quality of Jiangsu Province is mainly influenced by local pollution sources.

Spatio-temporal analysis of urban air pollutants throughout China during 2014-2019

Air pollution control has become the top priority of China's "green development" concept since 2013. The Chinese government has enacted a range of policies and statutes to control contaminant emissions and improve air quality. On the basis of the national air quality ground observation database, the spatial and temporal distribution of air quality index value (AQI), fine particulate matter (PM_2.5), coarse particles (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃) were explored in 336 cities throughout China from 2014 to 2019. AQI and most pollutants (except O₃) decreased in concentrations from 2014 to 2019. In 2019, all cities except Henan reached the level 2 of the ambient air quality index, and six cities had a lower ambient air quality index and reached the level 1. Spatially, higher pollutant concentrations were concentrated in large city clusters, whereas the areas with high O₃ concentration were found across the country. Furthermore, central heating was shown to have a negative impact on air quality. The observed AQI value, PM_2.5, PM₁₀, SO₂, NO₂, and CO concentrations were highest in north and northwest China and Henan province in central China. The correlations among pollutants suggest that the main sources of pollutants are fossil fuel combustion, industrial production, and motor vehicle emissions. The influence of meteorological factors on air quality, long-distance transportation, and the transformations of pollutants should be explored in future research.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11869-021-01043-5.

Predicting intraurban PM2.5 concentrations using enhanced machine learning approaches and incorporating human activity patterns

Urban areas contribute substantially to human exposure to ambient air pollution. Numerous statistical prediction models have been used to estimate ambient concentrations of fine particulate matter (PM_2.5) and other pollutants in urban environments, with some incorporating machine learning (ML) algorithms to improve predictive power. However, many ML approaches for predicting ambient pollutant concentrations to date have used principal component analysis (PCA) with traditional regression algorithms to explore linear correlations between variables and to reduce the dimensionality of the data. Moreover, while most urban air quality prediction models have traditionally incorporated explanatory variables such as meteorological, land use, transportation/mobility, and/or co-pollutant factors, recent research has shown that local emissions from building infrastructure may also be useful factors to consider in estimating urban pollutant concentrations. Here we propose an enhanced ML approach for predicting urban ambient PM_2.5 concentrations that hybridizes cascade and PCA methods to reduce the dimensionality of the data-space and explore nonlinear effects between variables. We test the approach using different durations of time series air quality datasets of hourly PM_2.5 concentrations from three air quality monitoring sites in different urban neighborhoods in Chicago, IL to explore the influence of dynamic human-related factors, including mobility (i.e., traffic) and building occupancy patterns, on model performance. We test 9 state-of-the-art ML algorithms to find the most effective algorithm for modeling intraurban PM_2.5 variations and we explore the relative importance of all sets of factors on intraurban air quality model performance. Results demonstrate that Gaussian-kernel support vector regression (SVR) was the most effective ML algorithm tested, improving accuracy by 118% compared to a traditional multiple linear regression (MLR) approach. Incorporating the enhanced approach with SVR algorithm increased model performance up to 18.4% for yearlong and 98.7% for month-long hourly datasets, respectively. Incorporating assumptions for human occupancy patterns in dominant building typologies resulted in improvements in model performance by between 4% and 37%. Combined, these innovations can be used to improve the performance and accuracy of urban air quality prediction models compared to conventional approaches.

Spatiotemporal variations and reduction of air pollutants during the COVID-19 pandemic in a megacity of Yangtze River Delta in China

In recent decades, air pollution has become an important environmental problem in the megacities of eastern China. How to control air pollution in megacities is still a challenging issue because of the complex pollutant sources, atmospheric chemistry, and meteorology. There is substantial uncertainty in accurately identifying the contributions of transport and local emissions to the air quality in megacities. The COVID-19 outbreak has prompted a nationwide public lockdown period and provides a valuable opportunity for understanding the sources and factors of air pollutants. The three-month period of continuous field observations for aerosol particles and gaseous pollutants, which extended from January 2020 to March 2020, covered urban, urban-industry, and suburban areas in the typical megacity of Hangzhou in the Yangtze River Delta in eastern China. In general, the concentrations of PM_2.5-10, PM_2.5, NO_x, SO₂, and CO reduced 58%, 47%, 83%, 11% and 30%, respectively, in the megacity during the COVID-Lock period. The reduction proportions of PM_2.5 and CO were generally higher in urban and urban-industry areas than those in suburban areas. NO_x exhibited the greatest reduction (>80%) among all the air pollutants, and the reduction was similar in the urban, urban-industry, and suburban areas. O₃ increased 102%-125% during the COVID-Lock period. The daytime elevation of the planetary boundary layer height can reduce 30% of the PM₁₀, PM_2.5, NO_x and CO concentrations on the ground in Hangzhou. During the long-range transport events, air pollutants on the regional scale likely contribute 40%-90% of the fine particles in the Hangzhou urban area. The findings highlight the future control and model forecasting of air pollutants in Hangzhou and similar megacities in eastern China.

In-vitro human lung cell injuries induced by urban PM2.5 during a severe air pollution episode: Variations associated with particle components

Environmental air pollutants pose significant threats to public health, especially the toxicity and diseases caused by the atmospheric fine particulate matters (PM_2.5). Since the health risks vary with both the concentrations and compositions of PM_2.5 which are determined by aerosol sources, how are their toxic effects relevant to the pollution level becomes an important issue, such as the haze episodes covering clean and polluted days. With the transition from non-pollution to pollution stage, daily PM_2.5 samples were collected from both the urban and industrial areas of Nanjing city, eastern China, covering a typical haze event in autumn-winter. Their unpropitious effects on human lung epithelial cells (A549) were compared by in vitro toxicity assays and chemical component analysis. Both air levels and cytotoxic effects of PM_2.5 varied with the transition of haze event. Although the concentration of PM_2.5 in air is of course the highest in pollution stage driven by local stable meteorological condition, unit mass of them posed higher toxicity (lower cell viability and higher IL-6) but induced lower cell oxidative (evidences of ROS and NQO1 mRNA expression) and inflammatory cytokine TNF-α responses than those particles during non-pollution stage. These patterns were explained by the metals and water-soluble components decreased with the haze development. Non-soluble particulate carbonaceous aerosol compositions might play a significant role in inducing cytotoxicity. Moreover, the regional pattern of episode pollution weakened the spatial variation within a city scale. Since the haze development intensified both the quantity and toxicity of PM_2.5 in air, the health risks of overall aerosol exposure were synthetically amplified during haze weather, so the increased air particles with higher toxic components from fuel combustion sources should be key targets of pollution control.

Air Pollution Characteristics and Health Risks in the Yangtze River Economic Belt, China during Winter

The air pollution characteristics of six ambient criteria pollutants, including particulate matter (PM) and trace gases, in 29 typical cities across the Yangtze River Economic Belt (YREB) from December 2017 to February 2018 are analyzed. The overall average mass concentrations of PM_2.5, PM₁₀, SO₂, CO, NO₂, and O₃ are 73, 104, 16, 1100, 47, and 62 µg/m³, respectively. PM_2.5, PM₁₀, and NO₂ are the dominant major pollutants to poor air quality, with nearly 83%, 86%, and 59%, exceeding the Chinese Ambient Air Quality Standard Grade I. The situation of PM pollution in the middle and lower reaches is more serious than that in the upper reaches, and the north bank is more severe than the south bank of the Yangtze River. Strong positive spatial correlations for PM concentrations between city pairs within 300 km is frequently observed. NO₂ pollution is primarily concentrated in the Suzhou-Wuxi-Changzhou urban agglomeration and surrounding areas. The health risks are assessed by the comparison of the classification of air pollution levels with three approaches: air quality index (AQI), aggregate AQI (AAQI), and health risk-based AQI (HAQI). When the AQI values escalate, the air pollution classifications based on the AAQI and HAQI values become more serious. The HAQI approach can better report the comprehensive health effects from multipollutant air pollution. The population-weighted HAQI data in the winter exhibit that 50%, 70%, and 80% of the population in the upstream, midstream, and downstream of the YREB are exposed to polluted air (HAQI > 100). The current air pollution status in YREB needs more effective efforts to improve the air quality.

Atmospheric fine particles in a typical coastal port of Yangtze River Delta

In recent decades, coastal ports have experienced rapid development and become an important economic and ecological hub in China. Atmospheric particle is a research hotspot in atmospheric environmental sciences in inland regions. However, few studies on the atmospheric particle were conducted in coastal port areas in China, which indeed suffers atmospheric particle pollution. Lack of the physicochemical characteristics of fine particles serves as an obstacle toward the accurate control for air pollution in the coastal port area in China. Here, a field observation was conducted in an important coastal port city in Yangtze River Delta from March 6 to March 19, 2019. The average PM_2.5 concentration was 63.7 ± 27.8 μg/m³ and NO₃^-, SO₄^2-, NH₄⁺, and organic matter accounted for ~60% of PM_2.5. Fe was the most abundant trace metal element and V as the ship emission indicator was detected. Transmission electron microscopy images showed that SK-rich, soot, Fe, SK-soot and SK-Fe were the major individual particles in the coastal port. V and soluble Fe were detected in sulfate coating of SK-Fe particles. We found that anthropogenic emissions, marine sea salt, and secondary atmosphere process were the major sources of fine particles. Backward trajectory analysis indicated that the dominant air masses were marine air mass, inland air mass from northern Zhejiang and inland-marine mixed air mass from Shandong and Shanghai during the sampling period. The findings can help us better understand the physicochemical properties of atmospheric fine particles in the coastal port of Eastern China.

Study on the characteristics of black carbon during atmospheric pollution conditions in Beijing

Black carbon (BC), not only has a negative impact on human health, but also contributes to visibility degradation and the attenuation of solar radiation due to light absorption. In this paper, we investigated the variations of BC concentration, BC optical characteristics and its effects on the physical and optical properties of atmospheric aerosols based on AERONET data during atmospheric pollution conditions in Beijing from 2012 to 2017. The results indicated that the average annual ground-level BC concentration and BC/PM_2.5 were 8.9 μg m^-3 and 6.7%, respectively, from 2012 to 2017 during atmospheric pollution conditions in Beijing. The annual mean ground-level BC concentration showed weak variation, but the monthly variation was pronounced during atmospheric pollution conditions. Moreover, the BC column concentration had a higher correlation with absorptive aerosol optical thickness (AAOT) at 870 nm (R² = 0.93) than 440 nm (R² = 0.73). The difference in AAOT between 440 nm and 870 nm was more significant under high BC column concentration. The seasonal variation of the BC column concentration that contributed to the AAOT at 870 nm displayed a consistent monthly average variation tendency. The BC column concentrations were divided into three segments of low, moderate, and high according to the results of the approximately normal distribution of the BC column concentration. Compared with high BC concentration, the single scattering albedo (SSA) and asymmetry parameter were enhanced by 0.05 and 0.04 in low BC concentrations, respectively. On the contrary, the fine mode fraction (FMF) was dropped by 12.5% in low BC concentrations. A higher BC concentration contributed to the enhancement in the AAOT and the extinction ratio of the fine mode aerosol. Meanwhile, the atmospheric particles' forward scattering ability was also attenuated under a high BC concentration.

The spatiotemporal characteristics of the air pollutants in China from 2015 to 2019

China’s rapid industrialization and urbanization have led to poor air quality, and air pollution has caused great concern among the Chinese public. Most analyses of air pollution trends in China are based on model simulations or satellite data. Studies using field observation data and focusing on the latest data from environmental monitoring stations covering the whole country to assess the latest trends of different pollutants in different regions are relatively rare. The State Council of China promulgated the toughest-ever Air Pollution Prevention and Control Action Plan (Action Plan) in 2013. This led to a major improvement in air quality. We use the hourly Air Quality Index (AQI) and mass concentrations of PM_2.5, PM₁₀, CO, NO₂, O₃, and SO₂ in 362 cities from 2015 to 2019, obtained from the Ministry of Ecology and Environment, to study their temporal and spatial changes and assess the effectiveness of the policy on the atmospheric environment since its promulgation and implementation. We found that the national and regional air quality in China continues to improve, with PM_2.5, PM₁₀, AQI, CO, and SO₂ exhibiting negative trends. However, O₃ and NO₂ pollution is an urgent problem that needs to be solved and the current control strategy for PM_2.5 will only partially reduce the PM_2.5 pollution in the western region. Although the implementation of "Action Plan" measures has effectively improved air quality, China’s air pollution is still serious and far from the WHO standard. Implementing measures for continuous and effective emissions control is still a top priority.

Fine particulate matter reduces the pluripotency and proliferation of human embryonic stem cells through ROS induced AKT and ERK signaling pathway

Epidemiological investigations have found that air fine particulate matter (PM) exposure not only causes respiratory and cardiovascular diseases in adults and children, but also affects embryonic development during pregnancy, leading to poor pregnancy outcomes. However, its exact molecular mechanism is still unclear. In this study, human embryonic stem cells (hESCs) were treated with PM at different concentrations then the morphology and proliferation capacity were measured. The mRNA and protein expression of NANOG and OCT4 were detected using quantitative PCR, immunofluorescence, western blotting, and flow cytometry. Reactive oxygen species (ROS) generation and AKT/ERK activation were also measured. Meanwhile, changes in ROS, the expression of NANOG, OCT4, and the AKT/ERK pathways were measured in the hESCs with or without pretreatment of ROS scavenger N-acetylcysteine (NAC) prior to PM exposure. After PM exposure, the proliferation capacity and expression of OCT4 and NANOG at the mRNA and protein levels were downregulated. The ROS level in the hESCs increased after PM exposure, but this increase in ROS was attenuated by pretreatment with NAC. Further analysis showed that the levels of phosphorylated AKT and ERK increased after PM exposure. After pretreatment with NAC, the phosphorylation levels of AKT and ERK, which are crucial for regulating the proliferation, pluripotency, and differentiation of hESC, were significantly attenuated compared with the non-NAC pretreated exposure group. These results suggest that PM exposure may reduce the proliferation and pluripotency of hESC through ROS-mediated AKT/ERK pathways, thereby affecting the long-term development of embryos.

The Impact of the Control Measures during the COVID-19 Outbreak on Air Pollution in China

The outbreak of the COVID-19 virus in Wuhan, China, in January 2020 just before the Spring Festival and subsequent country-wide measures to contain the virus, effectively resulted in the lock-down of the country. Most industries and businesses were closed, traffic was largely reduced, and people were restrained to their homes. This resulted in the reduction of emissions of trace gases and aerosols, the concentrations of which were strongly reduced in many cities around the country. Satellite imagery from the TROPOspheric Monitoring Instrument (TROPOMI) showed an enormous reduction of tropospheric NO2 concentrations, but aerosol optical depth (AOD), as a measure of the amount of aerosols, was less affected, likely due to the different formation mechanisms and the influence of meteorological factors. In this study, satellite data and ground-based observations were used together to estimate the separate effects of the Spring Festival and the COVID-19 containment measures on atmospheric composition in the winter of 2020. To achieve this, data were analyzed for a period from 30 days before to 60 days after the Spring Festivals in 2017–2020. This extended period of time, including similar periods in previous years, were selected to account for both the decreasing concentrations in response to air pollution control measures, and meteorological effects on concentrations of trace gases and aerosols. Satellite data from TROPOMI provided the spatial distributions over mainland China of the tropospheric vertical column density (VCD) of NO2, and VCD of SO2 and CO. The MODerate resolution Imaging Spectroradiometer (MODIS) provided the aerosol optical depth (AOD). The comparison of the satellite data for different periods showed a large reduction of, e.g., NO2 tropospheric VCDs due to the Spring Festival of up to 80% in some regions, and an additional reduction due to the COVID-19 containment measures of up to 70% in highly populated areas with intensive anthropogenic activities. In other areas, both effects are very small. Ground-based in situ observations from 26 provincial capitals provided concentrations of NO2, SO2, CO, O3, PM2.5, and PM10. The analysis of these data was focused on the situation in Wuhan, based on daily averaged concentrations. The NO2 concentrations started to decrease a few days before the Spring Festival and increased after about two weeks, except in 2020 when they continued to be low. SO2 concentrations behaved in a similar way, whereas CO, PM2.5, and PM10 also decreased during the Spring Festival but did not trace NO2 concentrations as SO2 did. As could be expected from atmospheric chemistry considerations, O3 concentrations increased. The analysis of the effects of the Spring Festival and the COVID-19 containment measures was complicated due to meteorological influences. Uncertainties contributing to the estimates of the different effects on the trace gas concentrations are discussed. The situation in Wuhan is compared with that in 26 provincial capitals based on 30-day averages for four years, showing different effects across China.

Spatio-Temporal Correlation Analysis of Air Quality in China: Evidence from Provincial Capitals Data

In China, public health awareness is growing as people get more concerned about the air quality. Based on the air quality index (AQI) of 31 provincial capital cities (2015–2018) in China, we studied the spatio-temporal correlations of air quality between cities. With spatial, temporal and spatio-temporal analysis, we systematically obtained many interesting results where the traditional analyses may be lacking. Firstly, the air quality of cities has spatial spillover and agglomeration effects and further the spatial correlation becomes higher with time. Secondly, there exists temporal correlation between the current AQI and its past values on multiple time scales, which shows certain periodicity. Thirdly, due to the changing characteristics of time, social activities and other factors affect the air quality positively. However, with the panel data model, the coefficients of spatio-temporal correlation vary for different cities.