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Evidence regarding the combined effects of green space and air pollutants on hypertension remains limited and complex. This study aims to investigate the varying effects of greenness under different air pollution levels in China, using data from the wave 2018 China Health and Retirement Longitudinal Study (CHARLS) involving 17 468 adults (aged ≥ 45 years). As a result, the prevalence rate of hypertension was 42.04%. Logistic regression analyses revealed the positive associations between air pollution concentrations at the city level and prevalent hypertension and the negative associations between NDVI and prevalent hypertension, all of which were more prominent in the populations of the eastern and rural regions. Notably, the negative effect of green space was greater at the lowest quartiles of each air pollutant (OR for PM2.5 quartiles = 0.724, 0.792, 0.740, and 0.931). Improving air quality and greenness could potentially reduce hypertension risk, and minimizing air pollution might optimize the protective effects of greenness. [ABSTRACT FROM AUTHOR]

The spatial and temporal variations in the atmospheric CO2 concentrations evidently respond to anthropogenic CO2 emission activities. NO2, a pollutant gas emitted from fossil fuel combustion, comes from the same emission sources as CO2. Exploiting the simultaneous emissions characteristics of NO2 and CO2, we proposed an XCO2 prediction approach to reconstruct XCO2 data based on the data-driven machine learning algorithm using multiple predictors, including satellite observation of atmospheric NO2, to resolve the issue of data gaps in satellite observation of XCO2. The prediction model showed good predictive performance in revealing CO2 concentrations in space and time, with a total deviation of 0.17 ± 1.17 ppm in the cross-validation and 1.03 ± 1.15 ppm compared to ground-based XCO2 measurements. As a result, the introduction of NO2 obtained better improvements in the CO2 concentration responding to the anthropogenic emissions in space. The reconstructed XCO2 data not only filled the gaps but also enhanced the signals of anthropogenic CO2 emissions by using NO2 data, as NO2 strongly responds to anthropogenic CO2 emissions (R2 = 0.92). Moreover, the predicted XCO2 data preferred to correct the abnormally low XCO2 retrievals at satellite observing footprints, where the XCO2_uncertainity field in the OCO-2 and OCO-3 products indicated a larger uncertainty in the inversion algorithm. [ABSTRACT FROM AUTHOR]

Studies related to air pollutants and spontaneous abortion in urban northwestern China are scarce, and the main exposure windows of pollutants acting on pregnant women are unclear. Case-control study. Data were collected from pregnant women in Tongchuan City from 2018 to 2019. A total of 289 cases of spontaneous abortion and 1156 cases of full-term labor were included and analyzed using a case–control study. Logistic regression models were developed to explore the relationship between air pollutants and spontaneous abortion after Chi square analysis and Air pollutant description. O 3 (odds ratio [OR] = 1.028) is a risk factor for spontaneous abortion throughout pregnancy. PM 2.5 (OR = 1.015), PM 10 (OR = 1.010), SO 2 (OR = 1.026), and NO 2 (OR = 1.028) are risk factors for spontaneous abortion in the 30 days before the last menstrual period. PM 2.5 (OR = 1.015), PM 10 (OR = 1.013), SO 2 (OR = 1.036), and NO 2 (OR = 1.033) are risk factors for spontaneous abortion in the 30–60 days before the last menstrual period. PM 2.5 (OR = 1.028), PM 10 (OR = 1.013), SO 2 (OR = 1.035), and NO 2 (OR = 1.059) are risk factors for spontaneous abortion in the 60–90 days before the last menstrual period. Exposure to high levels of air pollutants may be a cause of increased risk of spontaneous abortion, especially in the first trimester of the last menstrual period. [ABSTRACT FROM AUTHOR]

Air pollution is considered one of the greatest threats to human health. This study combines a land use regression (LUR) model with satellite measurements and a distributed-lagged non-linear model (DLNM). It aims to predict high-resolution ground-level concentrations of nitrogen dioxide (NO2) and particulate matter 2.5 (PM2.5) in the Yangtze River Delta (YRD) and reveal the mechanisms of influence between NO2 and PM2.5 and precursors and meteorological factors. Results showed that the annual average NO2 and PM2.5 in the YRD urban agglomeration 2019 were 39.5 µg/m3 and 37.5 µg/m3, respectively. The seasonal variation of NO2 and PM2.5 showed winter > spring > autumn > summer. There is a compelling and complex relationship between NO2 and PM2.5. Predictors indicate that latitude (Y), surface pressure (P), ozone (O3), carbon monoxide (CO), aerosol optical depth (AOD), residential, and rangeland have positive impacts on NO2 and PM2.5. In contrast, temperature (T), precipitation (PRE), and industrial trees hurt NO2 and PM2.5. DLNM model results show that NO2 and PM2.5 had significant associations with the included precursors and meteorological elements, with lagged and non-linear effects observed. Satellite data could help significantly increase the accuracy of LUR models; the R2 of tenfold cross-validation was enhanced by 0.18–0.22. In 2019, PM2.5 will be the dominant pollutant in the YRD, and NO2 showed a high value in the central and eastern parts of the YRD. High concentrations of NO2 and PM2.5 are present in 86% of the YRD, meaning that residents will have difficulty avoiding exposure to these two high pollution levels. [ABSTRACT FROM AUTHOR]

Currently, traffic-related sources are considered to be one of the major contributors to air pollutants in urban areas. As the number of motor vehicles increases, the impact of traffic-related air pollutants (TRAPs) on human health has also increased in recent years. People are easily exposed to TRAPs in their daily lives. However, long-term exposure to TRAPs can have adverse health effects. Mobile monitoring is more flexible compared to traditional urban monitoring stations and can effectively obtain the spatial variation characteristics of air pollutants. We mounted a sensor package on an electric bicycle and conducted mobile measurements of CO, NO2 and SO2 on a circular road in the center of Shaoxing, a city in the center of the Yangtze Delta, China. The CO, NO2 and SO2 concentrations were observed to be higher in the morning and evening rush hours, and the three pollutants show different seasonal and spatial variation characteristics. CO concentration was higher in urban arterial and crossroads. NO2 concentration was variable, alternating between high and low concentrations. SO2 concentration was relatively stable and aggregated. This study provides important information on the spatial and temporal variations of TRAPs, which helps commuters understand how to effectively reduce pollutant exposure during personal travel. [ABSTRACT FROM AUTHOR]

With the spread of the COVID-19 pandemic and the implementation of closure measures in 2020, population mobility and human activities have decreased, which has seriously impacted atmospheric quality. Huaibei City is an important coal and chemical production base in East China, which faces increasing environmental problems. The impact of anthropogenic activities on air quality in this area was investigated by comparing the COVID-19 lockdown in 2020 with the normal situation in 2021. Tropospheric NO2, HCHO and SO2 column densities were observed by ground-based multiple axis differential optical absorption spectroscopy (MAX-DOAS). In situ measurements for PM2.5, NO2, SO2 and O3 were also taken. The observation period was divided into four phases, the pre-lockdown period, phase 1 lockdown, phase 2 lockdown and the post-lockdown period. Ground-based MAX-DOAS results showed that tropospheric NO2, HCHO and SO2 column densities increased by 41, 14 and 14%, respectively, during phase 1 in 2021 vs. 2020. In situ results showed that NO2 and SO2 increased by 59 and 11%, respectively, during phase 1 in 2021 vs. 2020, but PM2.5 and O3 decreased by 15 and 17%, respectively. In the phase 2 period, due to the partial lifting of control measures, the concentration of pollutants did not significantly change. The weekly MAX-DOAS results showed that there was no obvious weekend effect of pollutants in the Huaibei area, and NO2, HCHO and SO2 had obvious diurnal variation characteristics. In addition, the relationship between the column densities and wind speed and direction in 2020 and 2021 was studied. The results showed that, in the absence of traffic control in 2021, elevated sources in the Eastern part of the city emitted large amounts of NO2. The observed ratios of HCHO to NO2 suggested that tropospheric ozone production involved NOX-limited scenarios. The correlation analysis between HCHO and different gases showed that HCHO mainly originated from primary emission sources related to SO2. [ABSTRACT FROM AUTHOR]

The vertical profiles of nitrogen dioxide (NO2) and formaldehyde (HCHO) in the troposphere at the Longfengshan (LFS) regional atmospheric background station (127°36′ E, 44°44′ N, 330.5 m above sea level) from 24 October 2020 to 13 October 2021 were retrieved from solar scattering spectra by multi-axis differential optical absorption spectroscopy (MAX-DOAS). We analyzed the temporal variations of NO2 and HCHO as well as the sensitivity of ozone (O3) production to the concentration ratio of HCHO to NO2. The largest NO2 volume mixing ratios (VMRs) occur in the near-surface layer for each month, with high values concentrated in the morning and evening. HCHO has an elevated layer around the altitude of 1.4 km consistently. The means ± standard deviations of vertical column densities (VCDs) and near-surface VMRs were 4.69 ± 3.72 ×1015 molecule·cm−2 and 1.22 ± 1.09 ppb for NO2, and they were 1.19 ± 8.35 × 1016 molecule·cm−2 and 2.41 ± 3.26 ppb for HCHO. The VCDs and near-surface VMRs for NO2 were high in the cold months and low in the warm months, while HCHO presented the opposite. The larger near-surface NO2 VMRs appeared in the condition associated with lower temperature and higher humidity, but this relationship was not found between HCHO and temperature. We also found the O3 production at the Longfengshan station was mainly in the NOx-limited regime. This is the first study presenting the vertical distributions of NO2 and HCHO in the regional background atmosphere of northeastern China, which are significant to enhancing the understanding of background atmospheric chemistry and regional ozone pollution processes. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO2) is an important precursor of atmospheric aerosol. Forecasting urban NO2 concentration is vital for effective control of air pollution. This paper proposes a hybrid deep learning model for predicting daily average NO2 concentrations on the next day, based on atmospheric pollutants, meteorological data, and historical data during 2014 to 2020 in five coastal cities of Shandong peninsula, northern China. A random Forest (RF) algorithm was used to select input variables to reduce data dimensionality trained by the sequence to sequence (Seq2Seq) the model and describe how the Seq2Seq model understands each predictor variable. The hybrid model combining an RF with Seq2Seq network (RF-S2S) was evaluated and achieved a Pearson's correlation coefficient of 0.93, a Nash–Sutcliffe coefficient (NS) of 0.79, a Root Mean Square Error (RMSE) of 5.85 µg/m3, a Mean Absolute Error (MAE) of 4.50 µg/m3, and a Mean Absolute Percentage Error (MAPE) of 20.86%. Feature selection by an RF model improves the performance of the Seq2Seq model, reducing errors by 19.7% (RMSE), 20.3% (MAE), and 29.3% (MAPE), respectively. Carbon monoxide (CO) and PM10 are two common, important features influencing the prediction of NO2 concentrations in coastal areas of northern China. The results of RF-S2S models can capture general trends and disruptions more accurately than can long-short term memory (LSTM) models with and without feature selection. The decreasing tendency of NO2 from 2014 to 2020 illustrated by the empirical mode decomposition (EMD) method is one important obstacle to improving the RF-S2S prediction accuracy. An EMD-based RF-S2S model could help to perform the short-term forecast of NO2 concentrations efficiently. [ABSTRACT FROM AUTHOR]

Pollutants in the atmosphere have been linked to poor pregnancy outcomes in women. However, such investigations are scarce in metropolitan northern China. The major exposure window of air pollution affecting pregnant women is also unknown. For the analysis, this retrospective cohort study enrolled 6960 pregnant women recorded at Tongchuan People's Hospital from January 2018 to December 2019. Pollutant concentration values from the nearest monitoring station to the pregnant women were used to estimate exposure doses for each exposure window. Logistic regression models were created to investigate the connection between pollutants and spontaneous abortion while controlling for confounding factors. PM2.5 was a risk factor for spontaneous abortion in T3 (30–60 days before the first day of the last menstrual period [LMP]), (OR: 1.305, 95% CI: 1.143–1.490) and T4 (60–90 days before the first day of the LMP),(OR: 1.450, 95% CI: 1.239–1.696) after controlling for covariates. In the same window, PM10 was a risk factor (OR: 1.308, 95% CI: 1.140–1.500), (OR: 1.386, 95% CI: 1.184–1.621). In T2 (30 days before the first day of the LMP), T3, and T4, SO2 was a risk factor for spontaneous abortion (OR: 1.185, 95% CI: 1.025–1.371), (OR: 1.219, 95% CI: 1.071–1.396), (OR: 1.202, 95% CI: 1.040–1.389). In T3 and T4, NO2 was a risk factor (OR: 1.171, 95% CI: 1.019– 1.346), (OR: 1.443, 95% CI: 1.259–1.655). In T1 (from the first day of the LMP to the date of abortion), O3 was found to be a risk factor (OR: 1.366, 95% CI: 1.226–1.521). Exposure to high levels of air pollutants before and during pregnancy may be a risk factor for spontaneous abortion in pregnant women. This study further illustrates the importance of reducing air pollution emissions. [ABSTRACT FROM AUTHOR]

Hyperspectral observations are used to retrieve high-resolution horizontal distribution and vertical profiles of trace gases (O3, NO2, HCHO, and SO2), thereby playing a vital role in monitoring the spatio-temporal distribution and transportation of atmospheric pollutants. These observations reflect air quality changes on global and regional scales, including China, thereby elucidating the impacts of anthropogenic and natural emissions on atmospheric composition and global climate change. The DaQi 02 (DQ02) satellite carries the Environmental Trace Gases Monitoring Instrument with Nadir and Limb modes (EMI-NL) onboard, which will simultaneously perform nadir and limb measurements of high-resolution ultraviolet and visible solar scattered light in the nadir and limb directions. Combined with the absorption of different trace gases in this wavelength band, this information can provide high-resolution horizontal and vertical distributions of trace gases. We examined the spectral measuring ability and instrument characteristics of both modules of EMI-NL by measuring different light sources and concentrations of the NO2 sample gas. In the nadir module test, when the NO2 sample gas concentration was 198 ppm and 513 ppm with scattered sunlight as the light source, the average relative errors of spatial pixels were 4.02% and 3.64%, respectively. At the NO2 sample gas concentration of 198 ppm with the integrating sphere as the light source, the average relative error of spatial pixels was −2.26%. In the limb module test, when the NO2 sample gas concentration was 198 ppm and 1000 ppm with the tungsten halogen lamp as the light source, the average relative errors of spatial pixels were −3.07% and 8.32%, respectively. When the NO2 sample gas concentration was 198 ppm and 1000 ppm with the integrating sphere as the light source, the spatial pixel average errors were −3.5% and 8.06%, respectively. The retrieved NO2 slant column density between different spatial pixels exhibited notable inconsistency in both modules, which could be used to estimate the stripe of spatial dimension. These results confirm the ability of EMI-NL to provide accurate spaceborne monitoring of NO2 globally. [ABSTRACT FROM AUTHOR]

• A control-oriented refined SCR-DeNO X model is established, which considers the NO 2 is indispensable. • A novel configuration is designed, which introduces a by-pass valve in front of the NH 3 injection in the SCR-DeNO X system. • Integral sliding mode control based on inversion algorithms is proposed to control the NH 3 coverage ratio of the two cells. To respond the China's "14th Five-Year Plan" on the control of nitrogen oxides (NO X) emission, urea-based selective catalytic reduction (SCR) system has been widely applied to reduce the NO X emission from the diesel engine. NO 2 is no longer a factor to be ignored while refining the SCR-DeNO X system model because of the ship diesel engine's long-term low temperature and oxygen-rich condition, which provides abundant conditions for the creation of NO 2. Therefore, a refined eight-state two-cell SCR-DeNO X model is established in this paper. Meanwhile, the trade-off between NO X emission and tailpipe ammonia (NH 3) slip is one of the major challenges which SCR-DeNO X system is facing. While meeting NO X emission standards, the requirement of limiting NH 3 slip to prevent secondary pollution is becoming increasingly urgent, which needs a better structure or controller. Therefore, a two-cell SCR-DeNO X system with a by-pass valve in front of the NH 3 injection is designed, integral sliding mode control based on inversion algorithm (IA_ISMC) is proposed for each cell. Based on the FTP-75 test cycle, there are three points of view are presented. Firstly, compared with the traditional six-state model, simulation results from the refined eight-state SCR-DeNO X model are closer to the experimental data. Secondly, the introduction of the by-pass structure has a mild impact on the NO X conversion efficiency, but can largely limit the NH 3 slip. Finally, the controller based on IA_ISMC effectively improves the tracking performance of NH 3 coverage ratio and reduces the cost of NH 3 injection. To sum up, the refined two-cell SCR-DeNO X system with a by-pass valve based on IA_ISMC can enhance SCR performance and lower costs. [ABSTRACT FROM AUTHOR]

There is a positive association between air pollution and lung cancer burden. This study aims to identify and examine lung cancer risks and mortality burdens associated with air pollutants, including PM10, NO2 and SO2, in seven eastern metropolises of China. The study population comprised a population from seven eastern metropolises of China. The yearly average values (YAV, mg/m3) of the PM10, NO2 and SO2 levels were extracted from China Statistical Yearbook (CSYB) for each selected city from 2006 to 2014. Data collected in the China Cancer Registry Annual Report (CCRAR) provide lung cancer incidence and mortality information. A two-level normal random intercept regression model was adopted to analyze the association between the lung cancer rates and individual air pollutant concentration within a five-year moving window of past exposure. The yearly average values of PM10, SO2 and NO2 significantly decreased from 2006 to 2014. Consistently, the male age-adjusted incidence rate (MAIR) and male age-adjusted mortality rate (MAMR) decreased significantly from 2006 to 2014.Air pollutants have a lag effect on lung cancer incidence and mortality for 2-3 years. NO2 has the significant association with MAIR (RR=1.57, 95% CI: 1.19-2.05, p=0.002), MAMR (RR=1.70, 95% CI: 1.32-2.18, p=0.0002) and female age-adjusted mortality rate (FAMR) (RR=1.27, 95% CI: 1.08-1.49, p=0.003). Our findings suggested that air pollutants may be related to the occurrence and mortality of lung cancer. NO2 was significantly associated with the risk of lung cancer, followed by SO2. Air pollutants have the strongest lag effect on the incidence and mortality of lung cancer within 2-3 years. [ABSTRACT FROM AUTHOR]

In the present study, a daily model is proposed for estimating the near-surface NO2 concentration in China, combining for the first time the Random Forest (RF) machine learning algorithm with the tropospheric NO2 columns from the TROPOspheric Monitoring Instrument (TropOMI) satellite and meteorological and NO2 data of surface sites in China for the year 2019. Furthermore, near-surface NO2 concentration data of ground sites during the COVID-19 outbreak from 1–5 February 2020 were used to verify the developed model. The daily model was verified by the ten-fold cross-validation method, revealing a coefficient of determination (R2) of 0.78 and root-mean-square error (RMSE) of 7.04 μg/m3, which are reasonable and also comparable to other published studies. In addition, our model showed that near-surface NO2 in China during the COVID-19 pandemic was significantly reduced compared with 2019, and these predictions were in good agreement with reference ground data. Our proposed model can also provide NO2 estimates for areas in western China where there are few ground monitoring sites. Therefore, all in all, our study findings suggest that the model established herein is suitable for estimating the daily NO2 concentration near the surface in China and, as such, can be used if there is a lack of surface sites and/or missing observations in some areas. [ABSTRACT FROM AUTHOR]

We performed a comprehensive and intensive field experiment including ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurement at Raoyang (115°44′ E, 38°14′ N; 20 m altitude) in summer (13 June–20 August) 2014. The NO2 and HCHO profiles retrieved by MAX-DOAS take on different vertical distribution shapes, with the former declining with the increasing altitude and the latter having an elevated layer. The average levels of vertical column densities (VCDs) and near-surface volume mixing ratios (VMRs) were 1.02 ± 0.51 × 1016 molec·cm−2 and 3.23 ± 2.70 ppb for NO2 and 2.32 ± 0.56 × 1016 molec·cm−2 and 5.62 ± 2.11 ppb for HCHO, respectively. The NO2 and HCHO levels are closely connected with meteorological conditions, with the larger NO2 VCDs being associated with lower temperature, higher relative humidity (RH) and lower planetary boundary layer height (PBLH). With respect to the diurnal variations of vertical distribution, the NO2 in the residual layer gradually disappeared from 1.2 km height to the surface during the period of 7:00–11:00 Beijing time (BJ), and the near-surface NO2 had larger VMRs in the early morning and evening than in the later morning and afternoon. An elevated HCHO layer was observed to occur persistently with the lifted layer height rising from ~0.5 km to ~1.0 km before 10:00 BJ; the near-surface HCHO VMRs gradually increased and peaked around 10:00 BJ. The ratios of HCHO to NO2 (RHCHO-NO2) were generally larger than two in the boundary layer from 11:00 BJ until 19:00 BJ, the time period when ozone photochemistry was most active. Thus, ozone (O3) production was mainly in the NOx-limited regime during the observation campaign, which was closely related to relatively high temperatures and low RH. The O3 production regimes also changed with the wind's direction. These results are significant to reveal the formation mechanism of O3 pollution and develop strategies for controlling the O3 photochemical pollution over the North China Plain. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Comprehensive air quality model with extensions (CAMx)-decoupled direct method (DDM) was used to simulate ozone-NO x -VOCs sensitivity of for May–November in 2016–2018 in China. Based on the relationship between the simulated ozone (O 3) sensitivity values and the ratio of formaldehyde (HCHO) to NO 2 (FNR) and the ratio of production rate of hydrogen peroxide (H 2 O 2) to production rate of nitric acid (HNO 3) (P H 2 O 2 / P HN O 3 ), the localized range of FNR and P H 2 O 2 / P HN O 3 thresholds in different regions in China were obtained. The overall simulated FNR values are about 1.640–2.520, and P H 2 O 2 / P HN O 3 values are about 0.540–0.830 for the transition regime. Model simulated O 3 sensitivities or region specific FNR or P H 2 O 2 / P HN O 3 thresholds should be applied to ensure the accurate local O 3 sensitivity regimes. Using the tropospheric column FNR values from ozone monitoring instrument (OMI) satellite data as an indicator with the simulated threshold values, the spatial distributions of O 3 formation regimes in China are determined. The O 3 sensitivity regimes from eastern to central China are gradually from VOC-limited, transition to NO x -limited spatially, and moving toward to transition or NO x -limited regime from 2005 to 2019 temporally. [Display omitted] [ABSTRACT FROM AUTHOR]

As an air pollutant closely related to urban traffic and heavy industrial capacity, the variation of NO2 (nitrogen dioxide) concentration can directly reflect the strength of socioeconomic activities. Using the weekly average results of daily product synthesis of tropospheric NO2 column concentrations from OMI (Ozone Monitoring Instrument) satellite inversion, a weekly-scale variation series of standardized socioeconomic activity index during the Spring Festival period of 2019–2021 is constructed. The results show that the OMI-NO2 satellite data are in good consistency with ground-based monitoring data; the Spring Festival holiday also suppresses socioeconomic activity in normal years, but the coronavirus disease 2019 (COVID-19) epidemic leads to an extended period of 2–3 weeks of weakened socioeconomic activity in China after the holiday, while the minimum value of socioeconomic activity intensity decreases by 0.12. Although socioeconomic activity is significantly suppressed in the short term, the intensity of socioeconomic activity rises steadily with the gradual resumption of work and production everywhere from the third week after the Chinese Spring Festival and has reached 60.91% of the highest level before the holiday in the seventh week after the holiday. OMI-NO2 satellite data can be used for a rapid assessment of the intensity of air pollution emissions and the level of socioeconomic activity in different regions. [ABSTRACT FROM AUTHOR]

This study investigated concentrations of PM 2.5 , PM 10 , SO 2 , NO 2 , CO and O 3 , and air quality index (AQI) values across 368 cities in mainland China during 2015–2018. The study further examined relationships of air pollution status with local industrial capacities and vehicle possessions. Strong correlations were found between industrial capacities (coal, pig iron, crude steel and rolled steel) and air pollution levels. Although statistical and significant reductions of PM 2.5 , PM 10 , SO 2 , NO 2 , CO and AQI values were observed in response to various laws and regulations in industrial sectors, both particle and gaseous pollutants still had annual average concentrations above recommended limits. In order to further reduce air pollution, more efforts can be done to control traffic emissions caused by minicars and heavy trucks, which was revealed after investigating 16 vehicle types. This was also consistent with the apparent air quality improvement during the COVID-19 lockdown period in China in 2020, despite industrial operations being still active at full capacities. [Display omitted] [ABSTRACT FROM AUTHOR]

Background: Cardiovascular disease (CVD) is the leading cause of death worldwide, with air pollution posing significant risks to cardiovascular health. The effect of air quality on heart failure (HF) readmission in acute myocardial infarction (AMI) patients is unclear.The aim of this study was to evaluate the role of a single measure of air pollution exposure collected on the day of first hospitalization., Methods: We retrospectively analyzed data from 12,857 acute coronary syndrome (ACS) patients (January 2015-March 2023). After multiple screenings, 4023 AMI patients were included. The air pollution data is updated by the automatic monitoring data of the national urban air quality monitoring stations in real time and synchronized to the China Environmental Monitoring Station. Cox proportional hazards regression assessed the impact of air quality indicators on admission and outcomes in 4013 AMI patients. A decision tree model identified the most susceptible groups., Results: After adjusting for confounders, NO2 (HR 1.009, 95% CI 1.004-1.015, P = 0.00066) and PM10 (HR 1.006, 95% CI 1.002-1.011, P = 0.00751) increased the risk of HF readmission in ST-segment elevation myocardial infarction (STEMI) patients. No significant effect was observed in non-STEMI (NSTEMI) patients (P > 0.05). STEMI patients had a 2.8-fold higher risk of HF readmission with NO2 > 13 μg/m3 (HR 2.857, 95% CI 1.439-5.670, P = 0.00269) and a 1.65-fold higher risk with PM10 > 55 μg/m3 (HR 1.654, 95% CI 1.124-2.434, P = 0.01064)., Conclusion: NO2 and PM10 are linked to increased HF readmission risk in STEMI patients, particularly when NO2 exceeds 13 μg/m3 and PM10 exceeds 55 μg/m3. Younger, less symptomatic male STEMI patients with fewer underlying conditions are more vulnerable to these pollutants., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Lingling Zhang reports financial support was provided by Xiangtan Central Hospital. Lingling Zhang reports a relationship with Xiangtan Central Hospital that includes: employment and non-financial support. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Thyroid nodules has become a significant public health issue worldwide with a rapidly increasing prevalence. However, its association with outdoor air pollution remains poorly understood. We aim to investigate the relationship between six outdoor air pollutants (PM2.5, PM10, NO2, SO2, CO, and O3) and the risk of thyroid nodules. Methods: We utilized a database including 4,920,536 participants who attended the annual physical examinations in the Meinian HealthCare Screening Center in 157 Chinese cities in 2017. City-specific concentrations of six pollutants (PM2.5, PM10, NO2, SO2, CO, and O3) from 2015 to 2017 were estimated based on the China's National Urban Air Quality Real Time Publishing Platform. Thyroid nodule was measured with ultrasound. Multivariable Logistic regression was used to examine the associations between air pollutants and thyroid nodules with adjustment for age, sex, education, smoking, body mass index, fasting blood glucose, triglyceride, low density lipoprotein cholesterol, high density lipoprotein cholesterol, urine iodine, gross domestic product, and thyroid stimulating hormone. We conducted stratified analyses to investigate potential effect modification by sex, age, and urine iodine groups. Results: Approximately 38% of the participants (1,869,742) were diagnosed with thyroid nodules. Each of the six air pollutants was significantly and linearly associated with the risk for thyroid nodules. The adjusted odds ratios [95% CI] for every increase of 10 μg/m3 for PM2.5, PM10, NO2, SO2, and O3 were 1.062 [1.061, 1.064], 1.04 [1.03, 1.04], 1.10 [1.09, 1.10], 1.11 [1.11, 1.12], and 1.151 [1.149, 1.154], respectively; The odds ratio for each increase of 1 mg/m3 for CO was 1.50 [1.49 to 1.52]. Furthermore, these associations were significantly higher in the participants who were men, younger, or having lower urine iodine level (p < 0.001). Conclusion: The six air pollutants may contribute to the high prevalence of thyroid nodules in China. [ABSTRACT FROM AUTHOR]

Short-duration high-strength human tourism activities (SHHTA) can result in more air pollution emissions owing to increase motor vehicle usage, energy consumption and cooking fume emissions. Because of the strong uncertainty of human tourism behaviour, it is difficult to accurately assess the impact of SHHTA on air quality of natural scenic spots. To overcome this difficulty, we propose a novel ensemble empirical mode decomposition and detrended cross-correlation analysis (EEMD-DCCA) model to assess the influence of short-duration high-strength human tourism activities (SHHTA) on air quality. Zhangjiajie in China was selected as the study area. Hourly concentrations of NO2 were analysed from 1 January 2016 to 31 December 2018 at two monitoring sites, in an urban area and a scenic spot. Through EEMD, the main modes of NO2 with short-duration high-frequency were obtained for both sites. The DCCA method was used to study the cross-correlation relationship between high-frequency modes of NO2 for the urban area and scenic spot. The results show that high-frequency modes of NO2 between the two sites displayed long-range cross-correlation at the 24-h time scale. Furthermore, the quantitative impacts of meteorological factors (e.g. precipitation, temperature, and wind speed) on the DCCA exponent for high-frequency modes of NO2 at the two sites were investigated. The novel model proposed in this study is not restricted by the uncertainty of pollution emission inventory. The relationship between meteorological factors and DCCA exponents corresponds to the hypothesis that NO2 pollution of the natural scenic spot mainly came from SHHTA. [ABSTRACT FROM AUTHOR]

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Multiple evidence has supported that air pollution exposure has detrimental effects on the cardiovascular and respiratory systems. However, most investigations focus on the general population, with limited research conducted on medically insured populations. To address this gap, the current research was designed to examine the acute effects of inhalable particulate matter (PM 2.5 and PM 10 ), nitrogen dioxide (NO 2 ), ground-level ozone (O 3 ), and sulfur dioxide (SO 2 ) on the incidence of upper respiratory tract infections (URTI), utilizing medical insurance data in Wuhan, China. Data on URTI were collected from the China Medical Insurance Basic Database for Wuhan covering the period from 2014 to 2018, while air pollutant data was gathered from ten national monitoring stations situated in Wuhan city. Statistical analysis was performed using generalized additive models for quasi-Poisson distribution with a log link function. The analysis indicated that except for ozone, higher exposure to four other pollutants (NO 2 , SO 2 , PM 2.5 , and PM 10 ) were significantly linked to an elevated risk of URTI, particularly during the previous 0-3 days and previous 0-4 days. Additionally, NO 2 and SO 2 were found to be positively linked with laryngitis. Furthermore, the effects of air pollutants on the risk of URTI were more pronounced during cold seasons than hot seasons. Notably, females and the employed population were more susceptible to infection than males and non-employed individuals. Our findings gave solid proof of the link between ambient air pollution exposure and the risk of URTI in medically insured populations., (© 2024. The Author(s) under exclusive licence to International Society of Biometeorology.)

To investigate the effect of nationwide restrictions due to COVID-19 on air quality in the Yangtze River Delta (YRD), China, we defined four periods named period I (January 1 to 23, 2020), period II (January 24 to February 23), period III (February 24 to April 7), and period IV (April 8 to May 31), which indicated normal period, lockdown period, regional work resumption period, and nationwide work resumption period, respectively. Hourly PM2.5, PM10, NO2, SO2, CO, and O3 in 41 cities in the YRD region were analyzed. Compared to period I, NO2 decreased by 58% during period II and increased in periods III and IV. SO2 remained constant during the four periods (7–8 μg/m3). Higher PM2.5 concentration was monitored during period II (41 μg/m3) when compared to period III (35 μg/m3), which was resulted from the enhanced secondary formation. Spatial distribution analysis further indicated that PM2.5 in the northern YRD during period II was higher than that during period III, whereas PM2.5 in the southern YRD in the period II was similar to that in period III. The results demonstrated that PM2.5 shows a nonlinear response to the reduction of its precursors, and this phenomenon varies in different areas. Compared to periods I (36 μg/m3) and III (64 μg/m3), relatively higher O3 during period II (64 μg/m3) was probably resulted from less NO emission and hence weakened NO titration effect. The study suggested that coordinated and balanced measures are needed to improve air quality. [ABSTRACT FROM AUTHOR]

Ozone pollution adversely affects human health. In recent years, ozone pollution has become an important research topic and a challenge. By combining differential absorption lidar (DIAL), meteorological gradient tower data, the Weather Research and Forecasting (WRF) model, and the backward trajectories model, changes in vertical ozone concentration, the influencing factors on ozone vertical distribution, and ozone sources were analyzed in Shenzhen from September 20 to 30, 2019. Results indicated that from September 25 to 30, there was obvious residual ozone and transport at night. The analysis of ozone concentration and meteorological factors shows that the temperatures of 291.3–301.8 K and the humidity of 19.4%–71.6% were conducive to ozone formation. As height increased from less than 0.1–0.3 km, the average ozone concentration increased, and the average NO2 concentration decreased. The analysis of potential ozone sources showed that ozone was mainly transported from the northeast of China and that the highest concentration of ozone mainly comes from southeast of Jiangxi province in southeastern China. Key Points: There was obvious residual ozone and transport at night from September 25 to 30The temperatures of 291.3–301.8 K and the humidity of 19.4%–71.6% were conducive to ozone formationOzone is mainly transported from the northeast of China [ABSTRACT FROM AUTHOR]

The human movement and economic activities have been drastically reduced due to the Coronavirus Disease 2019 (COVID‐19) outbreak, leading to the sharp decreases of pollutant emissions and remarkable air quality improvement. Nevertheless, however, the changes of gaseous pollutant concentrations and health effects across China during the COVID‐19 lockdown period remained poorly understood. Here, a random forest model was applied to assess the impact of COVID‐19 lockdown on pollutant concentrations and potential health effects. The results suggested that estimated NO2, SO2, and CO concentrations in China during January 23–March 31, 2020 decreased by 13.68%, 25.71%, and 7.42%, respectively compared with the same periods in 2018–2019. Nonetheless, the predicted 8‐h O3 concentrations across China suffered from 1.29% increases during this period. The avoided premature all‐cause, cardiovascular disease (CVD), respiratory disease (RD), and chronic obstructive pulmonary disease (COPD) mortalities induced by NO2 decrease during COVID‐19 lockdown period reached 3,954 (3,076–4,832), 635 (468–801), 612 (459–765), and 920 (653–1,186) cases. However, the increases of all‐cause, CVD, RD, and COPD mortalities due to O3 increase during COVID‐19 lockdown period achieved 462 (250–674), 79 (29–129), 40 (−25–105), and 52 (−34–138) cases. The natural experiment demonstrated the drastic emission reduction measures could significantly decrease the NO2, SO2, and CO concentrations, while they significantly elevated the O3 concentration. It is highly imperative to propose more coordinated air pollution control strategies to control O3 pollution. Plain Language Summary: The human movement and economic activities have been drastically reduced due to the outbreak of COVID‐19, and thus the air pollutant concentrations might suffer corresponding variation. Although some recent studies have assessed the effects of COVID‐19 lockdown on air quality, most of these studies only used the observed data at many monitoring sites, which generally show low spatial resolution. Moreover, these sites mainly focus on the urban areas, which cannot reflect the real air quality change status. To date, the changes of air pollutant concentrations and associated health effects at a high spatial resolution in China still remained unknown. The natural experiment provided an unprecedented chances to assess the response of air quality and health benefits (costs) to emission reduction, which provide scientific basis for air pollution prevention. This paper will appeal to a wide general readership and be of exceptional interest to the environmental health specialist. Key Points: The impacts of Coronavirus Disease 2019 lockdown on gaseous pollutants across China were investigatedThe 8‐h O3 level displayed slight increase, while other pollutants showed dramatic decreasesCOVID‐19 saved 3,954 lives due to NO2 decrease, but it claimed 462 lives linked with O3 increase [ABSTRACT FROM AUTHOR]

Objective: We aimed to examine the associations between ambient air pollutants and daily mortality in Northeast China from 2014 to 2018. Methods: A two-stage approach was used to estimate particulate matter with an aerodynamic diameter of 10 µm (PM10), nitrogen dioxide (NO10), and sulfur dioxide (SP10) exposure and daily mortality. Results: An increase of 10 µg/m³ of PM10 exposure and NO2 at lag of 0 to 16 days was associated with the cumulative relative risk of 1.011 (95% confidence interval [CI] 1.004, 1.019) and 1.026 (95% CI: 1.004, 1.049), respectively, in non-accident mortality. Meanwhile, significant association was observed in people aged under 60 years between SO2 exposure and respiratory mortality at lag of 0 to 9 days. Conclusions: Our findings strengthen the evidence of PM10 and [ABSTRACT FROM AUTHOR]

Background: Ambient air pollution is becoming a serious environmental problem in China. The results were inconsistent on that air pollution was a risk factor of preeclampsia in pregnancy.Methods: Total 116,042 pregnant women were enrolled from 22 hospitals in 10 cities of Hebei Province, China from January 1, 2015 to December 31, 2017. The parturients were divided into preeclampsia group (PE group) and non-preeclampsia group (non-PE group). The data of air pollutants, namely, particulate matter (PM)2.5, PM10, NO2, SO2, CO, O3 were collected from China Environmental Inspection Station.Results: Among the 116,042 pregnant women, 2988 (2.57%) pregnant women were diagnosed with preeclampsia. The concentrations of exposed PM2.5, PM10, NO2 and O3 in the PE group were significantly higher than those in the non-PE group, and they were risk factors of the PE group in the first and second trimester of pregnancy respectively. The concentrations of exposed SO2 and CO in PE patients and non-PE women were not different, but high concentration of these air pollutants were risk factors to PE in the second trimester.Conclusion: The exposure to PM2.5, PM10, NO2, O3 were risk factors for preeclampsia in the first and second trimester of pregnancy, while only at high level, SO2 and CO were risk factors for preeclampsia in the second trimester of pregnancy. [ABSTRACT FROM AUTHOR]

Efforts to stem the spread of COVID‐19 in China hinged on severe restrictions to human movement starting 23 January 2020 in Wuhan and subsequently to other provinces. Here, we quantify the ancillary impacts on air pollution and human health using inverse emissions estimates based on multiple satellite observations. We find that Chinese NOx emissions were reduced by 36% from early January to mid‐February, with more than 80% of reductions occurring after their respective lockdown in most provinces. The reduced precursor emissions increased surface ozone by up to 16 ppb over northern China but decreased PM2.5 by up to 23 μg m−3 nationwide. Changes in human exposure are associated with about 2,100 more ozone‐related and at least 60,000 fewer PM2.5‐related morbidity incidences, primarily from asthma cases, thereby augmenting efforts to reduce hospital admissions and alleviate negative impacts from potential delayed treatments. Plain Language Summary: Satellite measurements such as TROPOMI have already captured the public's attention through remarkable images of pollutant reductions. However, the inference of emissions must account for variations in atmospheric transport, chemical environment, and meteorology. To that end, we used an advanced chemical data assimilation system that incorporate these factors through ingestion of multiple chemical satellite and in situ observations into a chemical transport model. We then quantified the reductions in emissions attributable to COVID‐19 lockdown and determine the impact of those reduction on human health through pollutant exposure. We find that our Chinese NOx emission reductions had opposing air quality responses depending on timing, location, and pollutant. Our investigation showed increases in ozone‐related morbidity incidences in Northern China but larger nationwide reductions from PM2.5 that led to overall reductions in hospital admissions and alleviating negative impacts of delayed treatment. Key Points: We quantified the ancillary impacts of the COVID‐19 lockdown on air pollution and human health using multiple satellite observationsRapid reductions in Chinese NOx and SO2 emissions increased surface ozone by 16 ppb over northern China but decreased PM2.5 nationwideThese changes increased by about 60 ozone‐related but decreased by about 5,000 PM2.5‐related hospital admissions [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. 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.)

Knowledge of vertical distribution of air pollutants is important for understanding the mechanisms underlying haze pollution. In this paper, we characterize the vertical structure of air pollutants based on ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements in the wintertime in Hefei, East China. Both NO 2 and aerosol show exponentially decreasing profile with high concentrations concentrated close to the surface, suggesting the major emissions are at ground level. SO 2 profiles extend to slightly higher altitudes than NO 2 , which might be due to the SO 2 emissions from elevated point sources (e.g., power plants). Elevated HCHO was observed in the middle layer during noon time, probably indicating that the larger effect of the photochemical formation of atmospheric HCHO rather than direct HCHO emissions. Potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) models were used to evaluate the potential sources of Hefei's air pollution in the lower, middle and upper boundary layer. The results revealed that the major regional source of NO 2 and aerosol were located in the northern and eastern part of Anhui and the transport mainly occured in the lower layer. Transported SO 2 from the northwestern regions in the middle layer made great contribution to SO 2 in Hefei. HCHO was mostly produced locally while the transport of HCHO from potential source areas was negligible. To further explore the potential source contributions affecting haze pollution, PSCF and CWT analysis for heavy haze episodes suggested that the potential source areas for aerosol were almost located in the northern region, the northwestern region, and the YRD region. Our findings provide a more comprehensive understanding for regional transport through the study on potential sources at different altitudes, which are useful for designing collaborative air pollution control strategies on a regional scale. • Vertical distribution of air pollutants is investigated to identify potential sources at different altitudes during winter. • Both NO 2 and aerosol show exponentially decreasing profile with high concentrations concentrated close to the surface. • SO 2 and HCHO profile typically extended to higher altitudes than NO 2 and aerosol did during winter. • The main potential sources for aerosol were located in the northern region, the northwestern region, and the YRD region. • This study is useful for designing collaborative air pollution control strategies on a regional scale. [ABSTRACT FROM AUTHOR]

Climate change and air pollution are one of the global environmental problems. It is significant to grasp the air pollution situation of Western Europe in recent 10 years for its or the global pollution control. Based on the OMI tropospheric nitrogen dioxide (NO 2 ) column density data, the spatial and temporal distribution characteristics, variation trend, transmission path, and influencing factors of NO 2 in 15 countries in Western Europe from 2011 to 2022 are discussed in this paper. Meanwhile, the annual average spatial and temporal distribution in 2023 is predicted by the random forest (RF) model. The results showed that (1) the 12-year spatial distribution map showed an increasing trend from southwest to northeast, with the border area of the Netherlands and Germany and Milan as two high-value areas, and the overall trend over time was that the high-concentration area gradually shrank, the low-concentration area gradually expanded, and the annual average concentration gradually decreased. (2) The inter-month trend presents a "U" shape, with the mean NO 2 pollution ranking in winter > autumn > spring > summer. (3) Natural factors are one of the reasons affecting NO 2 ; for instance, NO 2 pollution has a strong positive correlation with the lifted index, relative humidity, and wind speed and a moderately strong negative correlation with precipitable water and air temperature. (4) Exogenous atmospheric transport is another important factor affecting the change of NO 2 pollution in Western Europe. The HYSPLIT model is used to analyze the backward trajectory of Milan, Italy, and Nijmegen, Netherlands, in the four seasons of 2022. Both are mainly influenced by westerly airflows, and therefore, the transport effect in the atmosphere brings air pollutants from westerly regions in the atmosphere., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Background The aim of this study was to investigate an association between birth defects and exposure to sulfur dioxide (SO2), nitrogen dioxide (NO2) and particles ≤10 μm in an aerodynamic diameter (PM10) during early pregnancy in Xi'an, China. Methods Birth defect data were from the Birth Defects Monitoring System of Xi'an, and data on ambient air pollutants during 2010–15 were from the Xi'an Environmental Protection Bureau. A generalized additive model (GAM) was used to investigate the relationship between birth defects and ambient air pollutants. Results Among the 8865 cases with birth defects analyzed, the overall incidence of birth defects was 117.33 per 10 000 infants. Ambient air pollutant exposure during the first trimester increased the risk of birth defects by 10.3% per 10 μg/m3 increment of NO2 and 3.4% per 10 μg/m3 increment of PM10. No significant association was found between birth defects and SO2. Moreover, NO2 increased risk of neural tube defects, congenital heart disease, congenital polydactyly, cleft palate, digestive system abnormalities and gastroschisis, and PM10 was associated with congenital heart disease and cleft lip with or without cleft palate. Conclusions Chinese women should avoid exposure to high levels of NO2 and PM10 during the first 3 months of pregnancy. [ABSTRACT FROM AUTHOR]

Ground-based two-dimensional (2D) Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements of nitrogen dioxide (NO 2) were collected continuously from June 2015 to May 2016 in Hefei, China. The spatial distribution of NO 2 concentrations showed that more air pollution was present in eastern and southeastern direction from the study site, and these areas were located in the center of the city and economic developing zone. The seasonal variations of NO 2 concentrations were distinct, and the highest levels were detected in winter, while the lowest levels were detected in summer. The mean concentration of NO 2 during the heating season (November–March of the following year) was 6.56 ppbv, while that during the non-heating season (April–October) was 4 ppbv. The highest concentrations of NO 2 , carbon monoxide (CO), and PM 2.5 were all observed during the heating season. The results from the potential source contribution function (PSCF) analysis suggested that NO 2 pollution in the summer was caused primarily by long-range transport from the east, while that in winter showed significant local influences. By combining the analyses of meteorological trajectory pressure profiles and aerosol optical depths (AODs), the sources of NO 2 during the heating season were found to be mainly local emissions, while those during the non-heating season were mainly from long-range transportation. Image 1 • NO 2 was monitored during 2015–2016 in Hefei, China, with 2D MAX-DOAS data. • High NO 2 levels were concentrated in urban and industrial areas. • Local emission sources were the dominant sources of NO 2 in winter. • Haze and NO 2 pollution had simultaneous variations during the heating seasons. [ABSTRACT FROM AUTHOR]

An ensemble of satellite measurements, statistic data from government and meteorological diagnosis in regional background site (Haikou, China) has revealed the spatial and temporal characteristics of NO2 and associated synoptic transport patterns over southern China from January 2013 to February 2014. The result are as follows. (1) Ozone Monitoring Instrument (OMI) satellite products have a good correlation with observation NO2 in Haikou. The correlation coefficients between Observation NO2 and tropospheric column NO2 and ratio of Tro NO2/ Tot NO2 (tropospheric column NO2/ total column NO2) have all passed the confidence level of 99.9% test. (2) Tro NO2 over southern China has an obvious seasonal variation, which is closely coupled with regional meteorology in each season. (3) NO2 concentration in Haikou revealed three pollution periods during December 2013 and January 2014. The variation of NO2 concentration in Haikou is related to the meteorological elements closely. (4) Compared to the monthly mean meteorological fields of the pollution periods, the results indicate that NO2 pollution event in Haikou is directly related to the exogenous transportation from PRD region. An ensemble analysis of meteorological dynamic factors, wind vectors and backward trajectories during the pollution periods further verifies this conclusion. [ABSTRACT FROM AUTHOR]

Background: Previous studies have typically explored the daily lagged relations between influenza and meteorology, but few have explored seasonally the monthly lagged relationship, interaction and multiple prediction between influenza and pollution. Our specific objectives are to evaluate the lagged and interaction effects of pollution factors and construct models for estimating influenza incidence in a hierarchical manner., Methods: Our researchers collect influenza case data from 2005 to 2018 with meteorological and contaminative factors in Northeast China. We develop a generalized additive model with up to 6 months of maximum lag to analyze the impact of pollution factors on influenza cases and their interaction effects. We employ LASSO regression to identify the most significant environmental factors and conduct multiple complex regression analysis. In addition, quantile regression is taken to model the relation between influenza morbidity and specific percentiles (or quantiles) of meteorological factors., Results: The influenza epidemic in Northeast China has shown an upward trend year by year. The excessive incidence of influenza in Northeast China may be attributed to the suspected primary air pollutant, NO 2 , which has been observed to have overall low levels during January, March, and June. The Age 15-24 group shows an increase in the relative risk of influenza with an increase in PM 2.5 concentration, with a lag of 0-6 months (ERR 1.08, 95% CI 0.10-2.07). In the quantitative analysis of the interaction model, PM 10 at the level of 100-120 μg/m 3 , PM 2.5 at the level of 60-80 μg/m 3 , and NO 2 at the level of 60 μg/m 3 or more have the greatest effect on the onset of influenza. The GPR model behaves better among prediction models., Conclusions: Exposure to the air pollutant NO 2 is associated with an increased risk of influenza with a cumulative lag effect. Prioritizing winter and spring pollution monitoring and influenza prediction modeling should be our focus., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Urolithiasis accounts for the highest incidence of all urologic-associated hospitalizations. However, few studies have explored the effect of nitrogen dioxide (NO 2 ) 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. NO 2 exposure was associated with an increased risk of hospitalization for urinary tract stones. For each 10-μg/m 3 increase and 1-day lag of NO 2 , 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 NO 2 , and the cost of NO 2 -related urolithiasis hospitalizations reached approximately 3,430,000 RMB. Stratified analysis showed that NO 2 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 NO 2 and risk of urolithiasis hospitalization was more pronounced during the warm season. NO 2 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., (© 2023. The Author(s).)

Air pollution is considered one of the greatest threats to human health. This study combines a land use regression (LUR) model with satellite measurements and a distributed-lagged non-linear model (DLNM). It aims to predict high-resolution ground-level concentrations of nitrogen dioxide (NO 2 ) and particulate matter 2.5 (PM 2.5 ) in the Yangtze River Delta (YRD) and reveal the mechanisms of influence between NO 2 and PM 2.5 and precursors and meteorological factors. Results showed that the annual average NO 2 and PM 2.5 in the YRD urban agglomeration 2019 were 39.5 µg/m 3 and 37.5 µg/m 3 , respectively. The seasonal variation of NO 2 and PM 2.5 showed winter > spring > autumn > summer. There is a compelling and complex relationship between NO 2 and PM 2.5 . Predictors indicate that latitude (Y), surface pressure (P), ozone (O 3 ), carbon monoxide (CO), aerosol optical depth (AOD), residential, and rangeland have positive impacts on NO 2 and PM 2.5 . In contrast, temperature (T), precipitation (PRE), and industrial trees hurt NO 2 and PM 2.5 . DLNM model results show that NO 2 and PM 2.5 had significant associations with the included precursors and meteorological elements, with lagged and non-linear effects observed. Satellite data could help significantly increase the accuracy of LUR models; the R 2 of tenfold cross-validation was enhanced by 0.18-0.22. In 2019, PM 2.5 will be the dominant pollutant in the YRD, and NO 2 showed a high value in the central and eastern parts of the YRD. High concentrations of NO 2 and PM 2.5 are present in 86% of the YRD, meaning that residents will have difficulty avoiding exposure to these two high pollution levels., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

In the past decades, continuous efforts have been made at a national level to reduce Nitrogen Dioxide (NO2) emissions in the atmosphere over China. However, public concern and related research mostly deal with tropospheric NO2 columns rather than ground-level NO2 concentrations, but actually ground-level NO2 concentrations are more closely related to anthropogenic emissions, and directly affect human health. This paper presents one method to derive the ground-level NO2 concentrations using the total column of NO2 observed from the Ozone Monitoring Instrument (OMI) and the simulations from the Community Multi-scale Air Quality (CMAQ) model in China. One year's worth of data from 2014 was processed and the results compared with ground-based NO2 measurements from a network of China's National Environmental Monitoring Centre (CNEMC). The standard deviation between ground-level NO2 concentrations over China, the CMAQ simulated measurements and in-situ measurements by CNEMC for January was 21.79 μg/m3, which was improved to a standard deviation of 18.90 μg/m3 between our method and CNEMC data. Correlation coefficients between the CMAQ simulation and in-situ measurements were 0.75 for January and July, and they were improved to 0.80 and 0.78, respectively. Our results revealed that the method presented in this paper can be used to better measure ground-level NO2 concentrations over China. [ABSTRACT FROM AUTHOR]

The analysis of the daily spatial patterns of near-surface Nitrogen dioxide (NO 2) concentrations can assist decision makers mitigate this common air pollutant in urban areas. However, comparative analysis of NO 2 estimates in different urban agglomerations of China is limited. In this study, a new linear mixed effect model (LME) with multi-source spatiotemporal data is proposed to estimate daily NO 2 concentrations at high accuracy based on the land-use regression (LUR) model and Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) products. In addition, three models for NO 2 concentration estimation were evaluated and compared in four Chinese urban agglomerations from 2018 to 2020, including the COVID-19 closed management period. Each model included a unique combination of methods and satellite NO 2 products: ModelⅠ: LUR model with OMI products; Model Ⅱ: LUR model with TropOMI products; Model Ⅱ: LME model with TropOMI products. The results show that the LME model outperformed the LUR model in all four urban agglomerations as the average RMSE decreased by 16.09% due to the consideration of atmospheric dispersion random effects, and using TropOMI instead of OMI products can improve the accuracy. Based on our NO 2 estimations, pollution hotspots were identified, and pollution anomalies during the COVID-19 period were explored for two periods; the lockdown and revenge pollution periods. The largest NO 2 pollution difference between the hotspot and non-hotspot areas occurred in the second period, especially in the heavy industrial urban agglomerations. • Comparative analysis of near-surface NO 2 estimation by three models are made in four urban regions. • In near-surface NO 2 estimation, LME outperforms LUR model, and TropOMI outperforms OMI. • The pollution hotspots are identified and accord with clusters of roads and key industrial enterprises. • The retaliatory pollution influences NO 2 more than the COVID-19 outbreak controls. • The range of variation of NO 2 is stronger in hot spots than in non-hot spots. [ABSTRACT FROM AUTHOR]

Background: The association between air pollutants and psychiatric disorders has been investigated in many countries. However, results for the association between air pollutants and emergency room (ER) visits for psychiatric disorders are inconsistent. Further, systematic large-scale studies relating to the same are lacking, especially in South Korea., Objective: We aimed to investigate the acute and short-term cumulative effect of air pollutants on ER visits for psychiatric disorders in South Korea., Methods: The data on nitrogen dioxide (NO 2 ) and particulate matter (PM 2.5 and PM 10 ) and ER visits due to nine representative psychiatric disorders were collected from eight major cities in South Korea for three years. We estimated the relative risk (RR) at lag 0 and a cumulative 11-day RR by increasing a 10-unit for PM and 0.01-unit for NO 2 using the Distributed Lag Nonlinear Model., Results: During the study period, a total of 79,092 ER visits for psychiatric disorders were identified and tested for association with NO 2 , PM 2.5 , and PM 10 . The RR at lag 0 of depression per 0.01-unit increase in NO 2 was the highest (3.127; 95% confidence interval [CI] 2.933 to 3.332) among the psychiatric disorders. The RRs at lag 0 of anxiety disorders per 10-unit increase in PM 2.5 (1.709; 95% CI 1.424 to 2.053) and PM 10 (2.168; 95% CI 1.957 to 2.403) were the highest among the psychiatric disorders., Significance: Air pollutants increased ER visits for psychiatric disorders with the highest RR of depression due to NO 2 and anxiety disorder due to PM 2.5 and PM 10 . These results contribute evidence to the positive association between ambient exposure to air pollution and aggravation of psychiatric disorders, indicating air pollution may be a modifiable risk factor in mental health management., Impact Statement: We investigated the effect of air pollution on emergency room visits caused by major psychiatric disorders in prominent cities in South Korea. Using the Distributed Lag Nonlinear Model, an advanced analysis method, we calculated the acute effect and short-term cumulative effect. Air pollutants increased ER visits for psychiatric disorders with the highest relative risk of depression due to NO 2 and anxiety disorder due to PM 2.5 and PM 10 . These results reveal an association between ambient exposure to air pollution and aggravation of psychiatric disorders and suggest that air pollution may be a modifiable risk factor in mental health management., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Objective: Emerging evidence has highlighted the possible links of environmental pollution with several cardiovascular diseases (CVDs). The current study aimed to explore the impact of short-term air pollution exposure on CHD hospitalization in Hefei., Methods: Data about the daily number of CHD admissions (from 2014 to 2021) were retrieved from the First Affiliated Hospital of Anhui Medical University. Air pollutants and meteorological data were obtained from the China Environmental Monitoring Station and the China Meteorological Data Service Center, respectively. The correlation between air pollution and CHD hospitalization was assessed using distributed lag non-linear model (DLNM) and Poisson generalized linear regression., Results: In the single-pollutant model, NO 2 , O 3 , and CO strongly correlated with CHD hospitalization rate. Specifically, exposure to NO 2 (lag0, relative risk [RR]: 1.013, 95%CI: 1.002-1.024, per 10 μg/m 3 increase) and CO (lag13, RR: 1.035, 95%CI: 1.001-1.071, per 1 μg/m 3 increase) revealed a positive correlation with an increased rate of CHD hospitalization. Interestingly, O 3 had a protective association with hospitalization of CHD (lag0, RR: 0.993, 95%CI: 0.988-0.999, per 10 μg/m 3 increase). Similar results, to those of the single-pollutant model, were revealed following verification using two-pollutant models. Subgroup analyses indicated that young people, women, and people in hot seasons were more susceptible to NO 2 exposure, while the elderly, women, and people in cold seasons were more susceptible to O 3 . Furthermore, the elderly were more susceptible to CO exposure., Conclusion: Overall, exposure to NO 2 and CO increases the rate of CHD hospitalization, but exposure to O 3 shows a protective association with the rate of CHD hospitalization. Therefore, early preventive measures against air pollutants should be applied to protect vulnerable patients with CHD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Feng, Lang, Chen, Harry Asena, Fang, Zhang, Jiang, Fang, Chen, He, Wang and Pan.)

While deep learning is an emerging trend in modeling spatiotemporal dynamics of air pollution, designing a high-performance architecture with various neural network components is difficult. This study aims to identify a suitable deep learning architecture for deriving spatiotemporal distributions of surface NO 2 across China. Based on satellite retrievals and geographic variables, we compared the performance of a series of deep learning models and a benchmark model (i.e., random forest) in predicting daily NO 2 during 2016–2017 on a 0.1° grid. The deep learning models tested were hybrids of full connection layers (FC), autoencoders (AE), residual networks (RES), and convolution layers (CNN). Their ensemble forms were also comparatively evaluated. The integrated results of the sample-, cell-, and date-based holdout tests show that the ensemble FC-AE-RES-CNN model (R 2 = 0.71; RMSE = 9.9 μg/m3) outperformed the random forest (R 2 = 0.67; RMSE = 10.8 μg/m3), but none of the single-form networks did (R 2 ranged from 0.56 to 0.61, and RMSE ranged from 11.6 to 12.2 μg/m3). The superior performance of the ensemble FC-AE-RES-CNN model could be attributed to its lower bias and lower variance, which benefited from the sophisticated model structure and the ensemble strategy. Consequently, the ensemble FC-AE-RES-CNN model predictions demonstrated a spatiotemporal distribution of surface NO 2 with higher fidelity. In light of the comprehensive comparisons, we expect that ensemble deep learning models will be substituting for traditional machine learning models in environmental spatiotemporal mapping applications. [Display omitted] • Ensemble deep learning is an emerging trend in spatiotemporal modeling. • Convolution and pooling layers incorporate spatial neighborhood information. • An ensemble of simple models is better than one complicated model. • An ensemble of complicated models outperforms an ensemble of simple models. [ABSTRACT FROM AUTHOR]

Field measurements were conducted to assess the indoor air quality in kitchens using traditional stove without chimney, improved stove with chimney, and LPG stove in rural Hubei Province, China. Results showed that without cooking activity, concentrations of NO2, SO2, and CO in all kitchens met the national standard of China. On the other hand, PM2.5 in kitchens using LPG stove and traditional stove was slightly higher than the standard during the period without cooking activity. Cooking activity increased PM2.5 mass concentration and particle number concentration in all kitchens, especially in kitchen using traditional stove. With cooking activity, PM2.5 mass concentrations were well above the standard. Increase in CO, NO2, and SO2 concentrations during cooking was not as significant as PM2.5. Except for CO in traditional stove, mean concentrations of CO, NO2, and SO2 were below the standard even during cooking. [ABSTRACT FROM AUTHOR]

In this paper, based on a sample selection rule and a Back Propagation (BP) neural network, a new model of forecasting daily SO2, NO2, and PM10 concentration in seven sites of Guangzhou was developed using data from January 2006 to April 2012. A meteorological similarity principle was applied in the development of the sample selection rule. The key meteorological factors influencing SO2, NO2, and PM10 daily concentrations as well as weight matrices and threshold matrices were determined. A basic model was then developed based on the improved BP neural network. Improving the basic model, identification of the factor variation consistency was added in the rule, and seven sets of sensitivity experiments in one of the seven sites were conducted to obtain the selected model. A comparison of the basic model from May 2011 to April 2012 in one site showed that the selected model for PM10 displayed better forecasting performance, with Mean Absolute Percentage Error (MAPE) values decreasing by 4% and R² values increasing from 0.53 to 0.68. Evaluations conducted at the six other sites revealed a similar performance. On the whole, the analysis showed that the models presented here could provide local authorities with reliable and precise predictions and alarms about air quality if used at an operational scale. [ABSTRACT FROM AUTHOR]

Ground-based Multi-Axis Differential Optical Absorption Spectroscopy (Max-DOAS) measurements of nitrogen dioxide (NO 2) were continuously obtained from January to November 2019 in northeastern China (NEC). Seasonal variations in the mean NO 2 vertical column densities (VCDs) were apparent, with a maximum of 2.9 × 1016 molecules cm−2 in the winter due to enhanced NO 2 emissions from coal-fired winter heating, a longer photochemical lifetime and atmospheric transport. Daily maximum and minimum NO 2 VCDs were observed, independent of the season, at around 11:00 and 13:00 local time, respectively, and the most obvious increases and decreases occurred in the winter and autumn, respectively. The mean diurnal NO 2 VCDs at 11:00 increased to at 08:00 by 1.6, 5.8, and 6.7 × 1015 molecules cm−2 in the summer, autumn and winter, respectively, due to increased NO 2 emissions, and then decreased by 2.8, 4.2, and 5.1 × 1015 molecules cm−2 at 13:00 in the spring, summer, and autumn, respectively. This was due to strong solar radiation and increased planetary boundary layer height. There was no obvious weekend effect, and the NO 2 VCDs only decreased by about 10% on the weekends. We evaluated the contributions of emissions and transport in the different seasons to the NO 2 VCDs using a generalized additive model, where the contributions of local emissions to the total in the spring, summer, autumn, and winter were 89 ± 12%, 92 ± 11%, 86 ± 12%, and 72 ± 16%, respectively. The contribution of regional transport reached 26% in the winter, and this high contribution value was mainly correlated with the northeast wind, which was due to the transport channel of air pollutants along the Changbai Mountains in NEC. The NO 2 /SO 2 ratio was used to identify NO 2 from industrial sources and vehicle exhaust. The contribution of industrial NO 2 VCD sources was >66.3 ± 16% in Shenyang due to the large amount of coal combustion from heavy industrial activity, which emitted large amounts of NO 2. Our results suggest that air quality management in Shenyang should consider reductions in local NO 2 emissions from industrial sources along with regional cooperative control. [Display omitted] • NO 2 vertical column densities (VCDs) peaked in winter due to enhanced heating emissions. • NO 2 VCDs showed an uptrend from 08:00 to 11:00 and had a slight decrease at noon. • NO 2 was mainly sourced from local industrial NO 2 emissions in Shenyang. • Regional transport from the northeastern direction contributed 26% to NO 2 in winter. [ABSTRACT FROM AUTHOR]

Ambient ozone pollution has been becoming severe and attributed to considerable health impacts in China. Nitrogen dioxide (NO 2) is involved in atmospheric ozone production while also affecting public health directly. Joint control ozone and NO 2 pollution would be of significance. This study quantitatively assessed the health impact attributed to ambient ozone and NO 2 pollution in 338 Chinese cities from 2015 to 2020. The results reveal the generally opposite trends of ozone- and NO 2 -related health impacts in China. From 2015–2020, respiratory and chronic obstructive pulmonary disease (COPD) health impacts attributed to ozone in 338 cities increased by 65.30% and 63.98%. The NO 2 -attributed health impacts decreased by 24.80% and 24.62%. In 2020, the ozone- and NO 2 -related respiratory health impacts were 3.96 million DALYs (disability-adjusted life years) and 1.47 million DALYs. High health impacts are concentrated in big cities and city clusters. In 2020, the sum of ozone- and NO 2 -related respiratory health impacts in the top 20 cities was 0.98 million DALYs and 0.44 million DALYs, accounting for 24.70% and 30.24% of the 338 cities. The population attribution fraction analysis identified the increasing distributional consistency of ozone and NO 2 -related health impacts, emphasizing the necessity and possible efficiency of ozone-NO 2 joint control. Emission source analysis based on gridded data provided a reference for understanding health impacts and developing targeted strategies. [Display omitted] • Ozone- and NO 2 -related health impacts in 338 Chinese cities are assessed. • Ozone-related health impacts in 338 Chinese cities increased more than 60% 2015–2020. • Ozone- and NO 2 -related health impacts in 2015 were roughly equivalent. • Ozone-related health impact has been more than 200% of that NO 2 -related by 2020. • North-south differentiation existed in health impact characteristic and source. [ABSTRACT FROM AUTHOR]

Vehicle and industrial emissions are responsible for a considerable share of urban air pollution concentrations. We conducted a comparative study in Dalian, China and Faisalabad, Pakistan to reevaluate NO2, SO2, and CO concentrations. Our study was conducted over the course of one year at different stations in both cities, and measured values were compared with ambient air quality standards such as NEQS-Pakistan, NAAQSUSEPA, CNAAQS-China, and global standard WHO. The NO2 concentrations in Faisalabad varied from 185- 262 μg/m³, whereas it was in the range of 44-133 μg/m³ in Dalian. The SO2 concentration was found in the range of 66-190 μg/m³ and 56-128 μg/m³, while CO varied from 5.4-22.3 mg/m³ and 0.3-2.8 mg/m³, respectively, in Faisalabad and Dalian. The annual average NO2, SO2, and CO concentrations were below the permissible limits in Dalian; however, the measured values were considerably higher in Faisalabad. Our results are the first to compare air quality of two countries at different stations and highlight the possibility of reducing air pollution by comparing conditions. [ABSTRACT FROM AUTHOR]

Many studies have shown that air pollution may be closely associated with increased morbidity and mortality due to COVID-19. It has been observed that exposure to air pollution leads to reduced immune response, thereby facilitating viral penetration and replication. In our study, we combined information on confirmed COVID-19 daily new cases (DNCs) in one of the most polluted regions in the European Union (EU) with air-quality monitoring data, including meteorological parameters (temperature, relative humidity, atmospheric pressure, wind speed, and direction) and concentrations of particulate matter (PM 10 and PM 2.5 ), sulfur dioxide (SO 2 ), nitrogen oxides (NO and NO 2 ), ozone (O 3 ), and carbon monoxide (CO). Additionally, the relationship between bacterial aerosol (BA) concentration and COVID-19 spread was analyzed. We confirmed a significant positive correlation ( p < 0.05) between NO 2 concentrations and numbers of confirmed DNCs and observed positive correlations ( p < 0.05) between BA concentrations and DNCs, which may point to coronavirus air transmission by surface deposits on bioaerosol particles. In addition, wind direction information was used to show that the highest numbers of DNCs were associated with the dominant wind directions in the region (southern and southwestern parts).