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Ambient particulate matter (PM) pollution is a leading environmental health threat worldwide. PM with an aerodynamic diameter ≤ 1.0 μm, also known as PM1, has been implicated in the morbidity and mortality of several cardiorespiratory and cerebrovascular diseases. However, previous studies have mostly focused on analyzing fine PM (PM2.5) associated with disease metrics, such as emergency department visits and mortality, rather than ultrafine PM, including PM1. This study aimed to evaluate the association between short-term PM1 exposure and hospital admissions (HAs) for all-cause diseases, chronic obstructive pulmonary disease (COPD), and respiratory infections (RIs), as well as the associated expenditures, using Beijing as a case study. Here, based on air pollution and hospital admission data in Beijing from 2015 to 2017, we performed a time-series analysis and meta-analysis. It was found that a 10 μg/m3 increase in the PM1 concentration significantly increased all-cause disease HAs by 0.07% (95% Confidence Interval (CI): [0, 0.14%]) in Beijing between 2015 and 2017, while the COPD and RI-related HAs were not significantly associated with short-term PM1 exposure. Meanwhile, we estimated the attributable number of HAs and hospital expenditures related to all-cause diseases. This study revealed that an average of 6644 (95% CI: [351, 12,917]) cases of HAs were attributable to ambient PM1, which was estimated to be associated with a 106 million CNY increase in hospital expenditure annually (95% CI: [5.6, 207]), accounting for 0.32% (95% CI: [0.02, 0.62%]) of the annual total expenses. The findings reported here highlight the underlying impact of ambient PM pollution on health risks and economic burden to society and indicate the need for further policy actions on public health. [ABSTRACT FROM AUTHOR]

The Lightning Mapping Imager (LMI) onboard the Fengyun-4A (FY-4A) satellite is the first independently developed satellite-borne lightning imager in China. It enables continuous lightning detection in China and surrounding areas, regardless of weather conditions. The FY-4A LMI uses a Charge-Coupled Device (CCD) array for lightning detection, and the accuracy of lightning positioning is influenced by cloud top height (CTH). In this study, we proposed an ellipsoid CTH parallax correction (ECPC) model for lightning positioning applicable to FY-4A LMI. The model utilizes CTH data from the Advanced Geosynchronous Radiation Imager (AGRI) on FY-4A to correct the lightning positioning data. According to the model, when the CTH is 12 km, the maximum deviation in lightning positioning caused by CTH in Beijing is approximately 0.1177° in the east–west direction and 0.0530° in the north–south direction, corresponding to a horizontal deviation of 13.1558 km, which exceeds the size of a single ground detection unit of the geostationary satellite lightning imager. Therefore, it is necessary to be corrected. A comparison with data from the Beijing Broadband Lightning Network (BLNET) and radar data shows that the corrected LMI data exhibit spatial distribution that is closer to the simultaneous BLNET lightning positioning data. The coordinate differences between the two datasets are significantly reduced, indicating higher consistency with radar data. The correction algorithm decreases the LMI lightning location deviation caused by CTH, thereby improving the accuracy and reliability of satellite lightning positioning data. The proposed ECPC model can be used for the real-time correction of lightning data when CTH is obtained at the same time, and it can be also used for the post-correction of space-based lightning detection with other cloud top height data. [ABSTRACT FROM AUTHOR]

Extensive researches have linked air pollutants with cardiovascular disease (CVD) and respiratory diseases (RD), however, there is limited evidence on causal effects of air pollutants on morbidity of CVD or RD with comorbidities, particularly diabetes mellitus in elder patients. We included hospital admissions for CVD or RD among elder (≥65 years) diabetic patients between 2014 and 2019 in Beijing. A time‐stratified case‐crossover design based on negative‐control exposure was used to assess causal associations of short‐term exposure to air pollutants with CVD and RD among diabetic patients with the maximum lag of 7 days. A random forest regression model was used to calculate the contribution magnitude of air pollutants. A total of 493,046 hospital admissions were recorded. Per 10 μg/m3 uptick in PM1, PM2.5, PM10, SO2, NO2, O3, and 1 mg/m3 in CO was associated with 0.29 (0.05, 0.53), 0.14 (0.02, 0.26), 0.06 (0.00, 0.12), 0.36 (0.01, 0.70), 0.21 (0.02, 0.40), −0.08 (−0.25, 0.09), and 4.59 (0.56, 8.61) causal effect estimator for admission of CVD among diabetic patients, corresponding to 0.12 (0.05, 0.18), 0.09 (0.05, 0.13), 0.05, 0.23 (0.06, 0.41), 0.10 (0.02, 0.19), −0.04 (−0.06, −0.01), and 3.91(1.81, 6.01) causal effect estimator for RD among diabetic patients. The effect of gaseous pollutants was higher than particulate pollutants in random forest model. Short‐term exposure to air pollution was causally associated with increased admission of CVD and RD among elder diabetic patients. Gaseous pollutants had a greater contribution to CVD and RD among elder diabetic patients. Plain Language Summary: Although researches for cardiovascular disease (CVD) and respiratory diseases (RD) with air pollutants are widely reported, there is little evidence for causal associations of air pollution with CVD combined with diabetes mellitus (CVD‐DM) and RD combined with diabetes mellitus (RD‐DM) in elder patients. We included elder hospital admissions (aged ≥65 years) on comorbid CVD‐DM and RD‐DM patients between 2014 and 2019 in Beijing, China, to assess associations and causal inferences between air pollutants with CVD‐DM and RD‐DM. Random forest regression model was used to calculate the contribution magnitude of air pollutants. We found that exposure to air pollution increased admissions of CVD‐DM and RD‐DM in the elderly. Gaseous pollutants had a greater contribution to CVD‐DM and RD‐DM. The finding may lead to policy changes to reduce air pollution concentration, particularly gaseous pollutants to decrease hospital admissions. Key Points: Investigated causal associations of air pollutants on comorbid elderly populationA stable and accurate effect of air pollution was gained base on large sample sizesThe effects of gaseous pollutants are stronger than those of PM for the elderly [ABSTRACT FROM AUTHOR]

The association between CO and chronic obstructive pulmonary disease (COPD) has been widely reported; however, the association among patients with type 2 diabetes mellitus (T2DM) or hypertension has remained largely unknown in China. Over‐dispersed generalized additive model was adopted to quantity the associations between CO and COPD with T2DM or hypertension. Based on principal diagnosis, COPD cases were identified according to the International Classification of Diseases (J44), and a history of T2DM and hypertension was coded as E12 and I10‐15, O10‐15, P29, respectively. A total of 459,258 COPD cases were recorded from 2014 to 2019. Each interquartile range uptick in CO at lag 03 corresponded to 0.21% (95%CI: 0.08%–0.34%), 0.39% (95%CI: 0.13%–0.65%), 0.29% (95%CI: 0.13%–0.45%) and 0.27% (95%CI: 0.12%–0.43%) increment in admissions for COPD, COPD with T2DM, COPD with hypertension and COPD with both T2DM and hypertension, respectively. The effects of CO on COPD with T2DM (Z = 0.77, P = 0.444), COPD with hypertension (Z = 0.19, P = 0.234) and COPD with T2DM and hypertension (Z = 0.61, P = 0.543) were insignificantly higher than that on COPD. Stratification analysis showed that females were more vulnerable than males except for T2DM group (COPD: Z = 3.49, P < 0.001; COPD with T2DM: Z = 0.176, P = 0.079; COPD with hypertension: Z = 2.48, P = 0.013; COPD with both T2DM and hypertension: Z = 2.44, P = 0.014); No statistically significant difference could be found between age groups (COPD: Z = 1.63, P = 0.104; COPD with T2DM: Z = 0.23, P = 0.821; COPD with hypertension: Z = 0.53, P = 0.595; COPD with both T2DM and hypertension: Z = 0.71, P = 0.476); Higher effects appeared in cold seasons than warm seasons on COPD (Z = 0.320, P < 0.001). This study demonstrated an increased risk of COPD with comorbidities related to CO exposure in Beijing. We further provided important information on lag patterns, susceptible subgroups, and sensitive seasons, as well as the characteristics of the exposure‐response curves. Plain Language Summary: The association between CO and chronic obstructive pulmonary disease (COPD) has been widely reported; however, the association among patients with type 2 diabetes mellitus (T2DM) or hypertension has remained largely unknown in China. The aim of this study was to investigate the acute adverse effect of CO on admission of COPD, and COPD with T2DM or hypertension in Beijing, China. Our findings revealed that short‐term exposure to CO significantly increased the risk of COPD and COPD comorbidities admissions. Females were more vulnerable to CO exposure among COPD with comorbidities (expect T2DM group). We found higher effects appeared in cold season than warm season among COPD group. The exposure response relationship showed that there was no significant threshold effect between CO and COPD admission. Key Points: Exposure to CO was associated with an increased risk of chronic obstructive pulmonary disease (COPD) with comorbiditiesFemales were more vulnerable to CO exposure among COPD with comorbiditiesHigher effect appeared in cold season than warm season among COPD group [ABSTRACT FROM AUTHOR]

There are limited studies on the prevalence and incidence of clinically diagnosed hypertrophic myocardiopathy (HCM) and its mortality in the Chinese population, and the projected population burden of HCM over the next decades. We collected data on HCM and its mortality from the Beijing Municipal Health Commission Information Center (BMHCIC) database and estimated the prevalence and incidence based on the whole Beijing population. Calculation of population trends was performed using annual percentage change (APC) and average annual percentage change (AAPC). Finally, future HCM incidence was built by modelling projection of HCM to the next decades using Poisson regression analysis and Gray Model 1,1(GM [1,1]). The prevalence of HCM was 0.0069% (95%CI, 0.0065–0.0072%; N = 1343) in 2010, rising to 0.076% (95% CI, 0.074–0.077%; N = 16,616) in 2019, and the incidence of HCM was 6.85 per 100 000 person-year in 2010, rising to 11.76 per 100 000 person-year in 2019. Males had higher prevalence and incidence of HCM than females. The APPC for the rising incidence of HCM was 5.8% and the expected numbers will double increase in 2029 by assuming the same increase trend as the last decades. HCM had increased annual incidence of HF (APPC: 8.4, 4.4–12.6, p < 0.05), and relatively stable annual incidence of mortality (APPC: 1.2%, − 2.3% to 4.8%, p > 0.05) during the studied period. Males had lower mortality (2.70% vs. 4.20%, p < 0.001) than females. The calculated HCM prevalence was much lower compared to prior screening studies from 2004, although the predicted HCM incidence would double over the next decades. HCM was associated with a stable risk of mortality during the studied period. [ABSTRACT FROM AUTHOR]

Rising temperatures can increase the risk of mental disorders. As climate change intensifies, the future disease burden due to mental disorders may be underestimated. Using data on the number of daily emergency department visits for mental disorders at 30 hospitals in Beijing, China during 2016–2018, the relationship between daily mean temperature and such visits was assessed using a quasi-Poisson model integrated with a distributed lag nonlinear model. Emergency department visits for mental disorders attributed to temperature changes were projected using 26 general circulation models under four climate change scenarios. Stratification analyses were then conducted by disease subtype, sex, and age. The results indicate that the temperature-related health burden from mental disorders was projected to increase consistently throughout the 21st century, mainly driven by high temperatures. The future temperature-related health burden was higher for patients with mental disorders due to the use of psychoactive substances and schizophrenia as well as for women and those aged <65 years. These findings enhance our knowledge of how climate change could affect mental well-being and can be used to advance and refine targeted approaches to mitigating and adapting to climate change with a view on addressing mental disorders. • Future temperature-related health burden for mental disorders (MDs) will increase. • High temperatures pose a greater future health burden for MDs. • Those with substance use disorders and schizophrenia were at higher risk. • Female patients and patients aged <65 years were more susceptible to temperature. [ABSTRACT FROM AUTHOR]

JingFenCui is a type of rhodonite jade from the Changping district of Beijing, China, which is a manganese skarn deposit formed through the metasomatism of the granite aplite and Cambrian limestone. The pink color of JingFenCui is richer and brighter than that of rhodonite jade from other deposits. The surface of JingFenCui exhibits dendritic iron and manganese oxides, which is the outstanding advantage of rhodonite jade for carving works. The zoning pattern of mineralogy between the contact zone with the wallrock is obvious. The main skarn minerals consist mainly of spessartine, diopside, augite, manganotremolite, clino-suenoite, rhodonite, galena, etc. Compared with rhodonite jade from the Makeng in Fujian and the Luziyuan in western Yunnan, vittinkiite (MnSiO3) is more concentrated in the rhodonite of the JingFenCui Deposit as a Mn-Ag-Pb-Zn-Fe polymetallic deposit. There is a good agreement among the rhodonite, amphibole, and galena in the spider diagrams of the trace elements. The results of trace elements and REE analysis show that the metallogenic fluids of the JingFenCui deposit are mainly from magma related to the Heixiongshan granite. It is suggested that the JingFenCui deposit is a manganese skarn deposit formed through the metasomatism of the manganese-bearing limestone and fine-grained granite of the Heixiongshan. [ABSTRACT FROM AUTHOR]

Soils in coal coking plants can contain both polycyclic aromatic hydrocarbons (PAHs) and heavy metals (HMs). In this study, we analyzed PAH-HM pollution of soil in a coking plant in Beijing, China using a risk quotient and Hakanson potential ecological risk assessment to evaluate the ecological risk of PAHs and HMs, respectively. In addition, a magnetic whole-cell bioreporter was used to detect ecological toxicity of soil samples. The ecological risk and ecological toxicity were also compared. We found serious PAH pollution in the samples as evidenced by total PAH values of up to 1,206.99 mg/kg that were mainly 4-ring PAH. Lead and chromium contamination was present at 245.83 and 169.60 mg/kg, respectively, which was higher than concentrations of other HMs, including arsenic, cadmium, and mercury. Most samples had PAH levels that could present a serious ecological risk, whereas nearly half of the samples had HM concentrations that would pose high ecological risks. In a bioreporter assay, the relative bioluminescence response ratio of soil samples ranged from 1.76 to 4.66, indicating that all samples showed ecological toxicity. In this study, we showed that bioreporters can be used to express the combined influence of all hazardous chemicals and demonstrated that bioreporters could be an efficient and rapid approach to assess biological toxicity of soils with co-contamination and provide a full-scale risk assessment of polluted soil combining risk assessment. [ABSTRACT FROM AUTHOR]

Identifying urban multifractal structures are helpful for understanding urban spatial organization patterns as complex systems. Multifractal analysis is a powerful tool to model multifractal structures. However, due to the use of statistical moments to delineate the multifractal spectrum for multifractal analysis, the great majority of existing studies cannot map urban multifractal structures to geographic space. Lack of geographic mapping makes it difficult to interpret the causes of the anomalous scaling characteristics of urban multifractal structures. For the few mappable multifractal structure modeling methods, they model multifractal structures from a global or local perspective that generates inadequate or redundant scaling characteristics. Here, a classification-based multifractal analysis method (CMFA) was proposed to overcome the shortcomings. It classifies the urban areas into zones according to the density of urban elements and builds up the multi-scaling relationships of urban elements for each zone. The corresponding multifractal structures can be mapped according to the spatial distribution of zones across scales. A case study was conducted to identify the multifractal structures of nighttime light in Beijing, China, to verify the CMFA method. In conclusion, when there are abnormal scaling characteristics reflected by the multifractal spectrum, the multifractal structure maps can diagnose the land use problems leading to disordered spatial organization patterns. Urban planners should focus on such problem land parcels and carry out urban renewal to optimize urban spatial structures. • A classification-based multifractal analysis is built to map multifractal structures. • Nighttime light imagery can be utilized as proxy to support urban multifractal study. • Multi-scaling characteristics of nighttime light in Beijing, China are described. [ABSTRACT FROM AUTHOR]

Traffic exhaust is a main source of fine particulate matter (PM 2.5) in cities. Heavy-duty diesel trucks (HDDTs), the primary mode of freight transport, contribute significantly to PM 2.5 , posing a great threat to public health. However, existing research based on dispersion models to simulate pollutant concentrations lacks high-spatiotemporal-resolution emission inventories of HDDTs as input data, and the public health effects of such emissions in different populations have not been thoroughly assessed. To fill this gap, we focused on Beijing as the research area and developed a high-resolution PM 2.5 emission inventory for HDDTs based on Global Navigation Satellite System-equipped vehicle trajectory data. We then simulated the fine-scale spatial distribution of diesel-related PM 2.5 and assessed the population exposure by integrating the dispersion model and population distributions. Further, we quantified the mortality attributable to noncommunicable diseases (NCDs) plus lower respiratory infections (LRIs) related to PM 2.5 emissions from HDDTs. Results showed that 3.3% of Beijing people lived in areas with high PM 2.5 HDDT emissions, which were near intercity highways. Furthermore, the estimated number of NCD + LRI annual premature deaths attributed to PM 2.5 HDDT emissions in Beijing was 339 (95% CI: 276–401). The NCD + LRI mortality increased with age, and deaths were more frequent in males than females. Our results aid the identification of HDDT PM 2.5 emission exposure hotspots for the formulation of effective mitigation measures and provide important insights into the adverse health impacts of HDDT emissions. [Display omitted] • High PM 2.5 exposure from heavy-duty diesel trucks affects 3.3% of Beijing residents. • Per-capita PM 2.5 exposure from heavy-duty diesel trucks is higher in 20–44 age group. • PM 2.5 from heavy-duty diesel trucks caused higher male mortality than female. [ABSTRACT FROM AUTHOR]

Scientific studies have identified various adverse effects of particulate matter (PM) on respiratory disease (RD) and type 2 diabetes (T2D). However, whether short-term exposure to PM triggers the onset of RD with T2D, compared with RD without T2D, has not been elucidated. A two-stage time-series study was conducted to evaluate the acute adverse effects of PM on admission for RD and for RD with and without T2D in Beijing, China, from 2014 to 2020. District-specific effects of PM 2.5 and PM 10 were estimated using the over-dispersed Poisson generalized addictive model after adjusting for weather conditions, day of the week, and long-term and seasonal trends. Meta-analyses were applied to pool the overall effects on overall and cause-specific RD, while the exposure-response (E-R) curves were evaluated using a cubic regression spline. A total of 1550,154 admission records for RD were retrieved during the study period. Meta-analysis suggested that per interquartile range upticks in the concentration of PM 2.5 corresponded to 1.91% (95% CI: 1.33–2.49%), 2.16% (95% CI: 1.08–3.25%), and 1.92% (95% CI: 1.46–2.39%) increments in admission for RD, RD with T2D, and RD without T2D, respectively, at lag 0–8 days, lag 8 days, and lag 8 days. The effect size of PM 2.5 was statistically significantly higher in the T2D group than in the group without T2D (z = 3.98, P < 0.01). The effect sizes of PM 10 were 3.86% (95% CI: 2.48–5.27%), 3.73% (95% CI: 1.72–5.79%), and 3.92% (95% CI: 2.65–5.21%), respectively, at lag 0–13 days, lag 13 days, and lag 13 days, respectively, and no statistically significant difference was observed between T2D groups (z = 0.24, P = 0.81). Significant difference was not observed between T2D groups for the associations of PM and different RD and could be found between three groups for effects of PM 10 on RD without T2D. The E-R curves varied by sex, age and T2D condition subgroups for the associations between PM and daily RD admissions. Short-term PM exposure was associated with increased RD admission with and without T2D, and the effect size of PM 2.5 was higher in patients with T2D than those without T2D. [Display omitted] • Short-term PM exposures were associated with increased admissions for RD and for RD with and without T2D. • The effect of PM 2.5 on RD was significantly higher in patients with T2D than those without T2D. • Significant difference could be found between three different RD groups for effects of PM 10 on RD without T2D. • E-R curves varied by sex, age and T2D conditions for the associations between PM and RD. [ABSTRACT FROM AUTHOR]

Evidence on the relationship between particulate matter air pollution and urinary system disease (UD) is scarce. This study aims to evaluate the associations between short-term exposures to PM 2.5 and PM 10 and risk of daily UD inpatient hospital admissions through the emergency room (ER-admissions) in Beijing. We obtained 41,203 weekday UD ER-admissions for secondary and tertiary hospitals in all 16 districts in Beijing during 2013–2018 from the Beijing Municipal Health Commission Information Center and obtained district-level air pollution concentrations based on 35 fixed monitoring stations in Beijing. We conducted a two-stage time-series analysis, with district-specific generalized linear models for each of Beijing's 16 districts, followed by random effects meta-analysis to obtain pooled risk estimates. We evaluated lagged and cumulative associations up to 30 days. In single-pollutant models, for both PM 2.5 and PM 10 , cumulative exposure averaged over the day of admission and the previous 10 days (lag 0–10 days) showed the strongest association, with per interquartile range increases of PM 2.5 or PM 10 concentrations associated with a 7.5 % (95 % confidence interval [CI]: 3.0 %–12.2 %) or 6.0 % (95 % CI: 1.1 %–11.2 %) increased risk of daily UD hospital admissions, respectively. The risk estimates were robust to adjustment for co-pollutants and to a variety of sensitivity analyses. However, due to the strong correlation between PM 2.5 and PM 10 concentrations, we were unable to disentangle the respective relationships between these two exposures and UD risk. In this study, we found that short-term exposures to PM 2.5 and PM 10 are risk factors for UD morbidity and that cumulative exposure to PM pollution over a period of one to two weeks (i.e., 11 days) could be more important for UD risk than transient exposure during each of the respective single days. [Display omitted] • Short-term exposures to PM 2.5 and PM 10 are risk factors for urinary system disease. • Cumulative exposure over 11 days showed the strongest associations. • We were unable to disentangle the PM 2.5 versus PM 10 association. [ABSTRACT FROM AUTHOR]

The health effect of particulate matter pollution on stroke has been widely examined; however, the effect among patients with comorbid type 2 diabetes (T2D) in developing countries has remained largely unknown. A time-series study was conducted to investigate the short-term effect of fine particulate matter (PM 2.5) and inhalable particulate matter (PM 10) on hospital admissions for stroke among patients with T2D in Beijing, China, from 2014 to 2018. An over-dispersed Poisson generalized additive model was employed to adjust for important covariates, such as weather conditions and long-term and seasonal trends. A total of 159,298 hospital admissions for stroke comorbid with T2D were reported. Approximately linear exposure-response curves were observed for PM 2.5 and PM 10 in relation to stroke admissions among T2D patients. A 10 μg/m3 increase in the four-day moving average of PM 2.5 and PM 10 was associated with 0.14% (95% confidence interval [CI]: 0.05–0.23%) and 0.14% (95% CI: 0.06–0.22%) incremental increases in stroke admissions among T2D patients, respectively. A 10 μg/m3 increase in PM 2.5 in the two-day moving average corresponded to a 0.72% (95% CI: 0.02–1.42%) incremental increase in hemorrhagic stroke, and a 10 μg/m3 increase in PM 10 in the four-day moving average corresponded to a 0.14% (95% CI: 0.06–0.22%) incremental increase in ischemic stroke. High particulate matter might be a risk factor for stroke among patients with T2D. PM 2.5 and PM 10 have a linear exposure-response relationship with stroke among T2D patients. The study provided evidence of the risk of stroke due to particulate matter pollution among patients with comorbid T2D. • The health effect of particulate matter on stroke among T2D patients in developing countries has remained largely unknown. • Exposure to particulate matter is associated an increased risk of stroke admissions among T2D patients. • Approximately linear exposure-response curves were found between PM 2.5 and PM 10 with stroke admissions among T2D patients. • The study provided evidence of the risk of stroke due to particulate matter pollution among T2D patients. [ABSTRACT FROM AUTHOR]

Heavy-duty diesel trucks (HDDTs) cause serious pollution, and a high spatiotemporal resolution emissions inventory is a valuable assessment tool for use in quantitatively understanding the emissions mechanisms of HDDTs and scientifically developing associated emissions reduction measures. This study aims to comprehensively utilize multi-source spatiotemporal data on transportation—including fine-scale trajectories of HDDTs, road traffic conditions, and attribute data for road networks and HDDTs—supplemented by relatively mature vehicle pollution emissions models to establish a high spatiotemporal resolution emissions inventory for HDDTs in Beijing using a bottom-up approach. Spatial statistical techniques, including spatial autocorrelation, high/low clustering, and outlier analysis, are also used to explore the spatiotemporal distribution pattern of pollution emissions in the city. The results showed the following: (1) spatially, nitrogen oxide (NO x) and particulate matter (PM) emission hotspots spread from the Beijing sixth-ring roads to the fourth-ring roads from daytime to nighttime. The road segments with high emissions intensities have pronounced spatial agglomeration effects at night, but these are scattered during daytime. (2) Temporally, total HDDT NO x emissions are consistent with the traffic volume trends and are lower during major festivals. The highest NO x emissions occur at intercity highways, and this reflects the severe impact that intercity freight traffic has on air quality. The dominant HDDT NO x emissions are from vehicles belonging to the China 4 emissions standard. (3) NO x and PM emissions have a significant spatial autocorrelation and exhibit high-value clustering as a whole. (4) At different time intervals, the distribution of High-High/Low-Low clustering and outliers of NO x and PM in the road network is consistent with the spatial distribution of the pollutant emission intensity. The High-Low outlier is mainly distributed within the fourth-ring roads, and the number gradually reduces between night and day. The Low-High outlier is affected by the heterogeneous distribution of HDDTs and exhibits discontinuous distribution characteristics. Our results effectively evaluate Beijing's emissions control measures for HDDTs and provide a scientific decision-making basis for developing targeted emission reduction strategies for HDDTs. • A high-resolution emissions inventory of heavy-duty diesel trucks was established. • Emissions' spatiotemporal distribution patterns/trends in Beijing were clarified. • Policy implications to control emissions from heavy-duty diesel trucks are noted. [ABSTRACT FROM AUTHOR]

In this study, a one-dimensional variational (1D-VAR) retrieval system is proposed to simultaneously retrieve temperature and humidity atmospheric profiles under clear-sky conditions. Our technique requires observations from the Fengyun-3D Hyperspectral Infrared Radiation Atmospheric Sounding (HIRAS) satellite combined with the Weather Research and Forecast (WRF) model. In the method, the radiative transfer for the TIROS Operational Vertical Sounder (TOVS (RTTOV) model is also used as a forward observation operator. The accuracy of our approach was evaluated using as a case study the region of Beijing in China. Predicted temperature and humidity profiles were compared against ERA-Interim data, which was used as reference. Mean bias (MB) of the temperature profiles varied between −0.8 K to 0.9 K, while the root-mean-square error (RMSE) ranged from 0.5 K to 2.6 K. In the boundary layer, the 1D-VAR algorithm performed better compared with the first guess. In the middle troposphere, the retrievals were more dependent on the first guess. With respect to relative humidity predictions, the accuracy of the evaluation of the whole troposphere was improved with the inclusion of the satellite observations, reporting an MB varying from −5.68% to 2.83%. Compared with Atmospheric Infrared Sounder's (AIRS') products, our predicted temperature profiles showed a very good consistency and the humidity predictions were also of an acceptable prediction accuracy. All in all, results clearly evidenced the promising potential of our proposed approach for retrieving temperature and humidity profiles under clear-sky conditions. [ABSTRACT FROM AUTHOR]

PM 2.5 emissions from heavy-duty diesel vehicles (HDDTs) pose a serious threat to human health. Existing researches difficult to capture the spatiotemporal evolution patterns of pollutant concentrations, resulting in the health impacts of pollutant emissions under different spatiotemporal patterns remain unclear. To fill this gap, this study explores the spatiotemporal evolution patterns of PM 2.5 concentrations emitted by HDDTs from a spatiotemporal integration perspective, utilizing a three-dimensional spatiotemporal cube model. On this basis, the disparities in health impacts and economic losses under various spatiotemporal patterns are quantified. The results show that: 1) Differences in spatiotemporal patterns of PM 2.5 emissions from HDDTs have obvious public health impacts. 55% of the areas exhibit the Oscillating Hot Spot pattern, with health impacts and economic losses accounting for over 90% of the total. High-High Cluster pattern accounts for 26% of all spatiotemporal cluster patterns, but their health and economic consequences attribute over 80% of the total. 2) Short-term exposure to PM 2.5 emissions from HDDTs have resulted in substantial human health impacts and economic losses that cannot be ignored. The results of this study can assist decision-makers in identifying spatiotemporal patterns with high health impacts and economic losses of HDDT emissions, and providing decision support for the formulation of health-oriented environmentally sustainable policies. [Display omitted] • PM 2.5 emission patterns of heavy-duty diesel trucks were identified. • Differences in PM 2.5 emission patterns have obvious health impacts. • PM 2.5 emissions from heavy-duty diesel trucks showed 6 hotspots and 5 clusters. • Oscillating Hot Spot accounted for over 90% of health impacts and economic losses. • High-High Cluster accounted for over 80% of health impacts and economic losses. [ABSTRACT FROM AUTHOR]

[Display omitted] • PM 2.5 concentrations of heavy-duty diesel trucks were simulated at high resolution. • The health and economic benefits of the emission control scenarios were analyzed. • Agglomeration effects and emission hotspots of PM 2.5 concentrations were mined. • Updating the emission standard to China 6 would avoid 611 premature deaths. PM 2.5 emissions from heavy-duty diesel trucks (HDDTs) have a significant impact on air quality, human health, and climate change, and seriously threaten the UN Sustainable Development Goals. Globally, a series of emission control measures have been implemented to reduce pollution emissions from HDDTs. Current studies assessing the impact of these measures on air quality and human health have mainly used coarse-grained emission data as input to dispersion model, resulting in the inability to capture the spatiotemporal variability of pollutant concentrations and tending to increase the uncertainty of health impact assessment results. In this study, we quantified the impact of pollution control policies for HDDTs in Beijing on PM 2.5 concentrations, human health, and economic losses by integrating policy scenario analysis, pollution dispersion simulation, public health impact and economic benefit assessment models, supported by high spatiotemporal resolution emission data from HDDTs. The results show that PM 2.5 concentrations from HDDTs exhibit significant spatial aggregation characteristics, with the intensity of aggregation at night being about twice as high as that during the day. The emission hotspots are mainly concentrated in the sixth, fifth and fourth rings and major highways. Compared to the "business as usual" scenario in 2018, the current policy of updating the fuel standard to China VI and the emission standard to China 6 can reduce PM 2.5 concentrations by 96.72%, thereby avoiding 612 premature deaths, which is equivalent to obtaining economic benefits of 1.65 billion CNY. This study further emphasizes the importance of high spatiotemporal resolution emission data during traffic dispersion modeling. The results can help improve the understanding of the effectiveness of emission reduction measures for HDDTs from a health benefit perspective. [ABSTRACT FROM AUTHOR]

To examine the association between urban neighborhood disorder and the recurrence risk of patients with acute myocardial infarction (AMI) in central Beijing, China. Recurrent AMI was identified by the Beijing Monitoring System for Cardiovascular Diseases through the end of 2019 for patients discharged with AMI between 2007 and 2017. Cox proportional hazards models were performed to estimate associations between neighborhood disorder and AMI recurrence. Of 66,238 AMI patients, 11,872 had a recurrent event, and 3117 died from AMI during a median follow-up of 5.92 years. After covariate adjustment, AMI patients living in the high tertile of neighborhood disorder had a higher recurrence risk (hazard ratio [HR] 1.08, 95 % confidence interval [CI], 1.03–1.14) compared with those in the low tertile. A stronger association was noted for fatal recurrent AMI (HR 1.21, 95 % CI 1.10–1.34). The association was mainly observed in females (HR 1.04, 95 % CI: 1.02 to 1.06). Serious neighborhood disorder may contribute to higher recurrence risk, particularly fatal recurrence, among AMI patients. Policies to eliminate neighborhood disorders may play an important role in the secondary prevention of cardiovascular disease. • The association between neighborhood disorder and the recurrence risk of patients with AMI in central Beijing was examined. • AMI patients living in high neighborhood disorder area had a higher recurrence risk. • A stronger association was noted for fatal recurrent AMI. • The association was mainly observed in females. [ABSTRACT FROM AUTHOR]

Extensive studies have linked PM 2.5 and PM 10 with respiratory diseases (RD). However, few is known about causal association between PM 1 and morbidity of RD. We aimed to assess the causal effects of PM 1 on cause-specific RD. Hospital admission data were obtained for RD during 2014 and 2019 in Beijing, China. Negative control exposure and extreme gradient boosting with SHapley Additive exPlanation was used to explore the causality and contribution between PM 1 and RD. Stratified analysis by gender, age, and season was conducted. A total of 1,183,591 admissions for RD were recorded. Per interquartile range (28 μg/m3) uptick in concentration of PM 1 corresponded to a 3.08% [95% confidence interval (CI): 1.66%–4.52%] increment in morbidity of total RD. And that was 4.47% (95% CI: 2.46%–6.52%) and 0.15% (95% CI: 1.44%-1.78%), for COPD and asthma, respectively. Significantly positive causal associations were observed for PM 1 with total RD and COPD. Females and the elderly had higher effects on total RD, COPD, and asthma only in the warm months (Z = 3.03, P = 0.002; Z = 4.01, P < 0.001; Z = 3.92, P < 0.001; Z = 2.11, P = 0.035; Z = 2.44, P = 0.015). Contribution of PM 1 ranked first, second and second for total RD, COPD, and asthma among air pollutants. PM 1 was causally associated with increased morbidity of total RD and COPD, but not causally associated with asthma. Females and the elderly were more vulnerable to PM 1 -associated effects on RD. • Exposure to PM 1 was causally associated with increased morbidity of COPD. • Females and the elderly were more vulnerable to PM 1 -associated effects. • PM 1 contributed higher to respiratory diseases, especially compared to other PM. [ABSTRACT FROM AUTHOR]

Nonlocal heavy-duty diesel trucks (HDDTs) transported across regions cause serious pollution in local atmospheric environments. However, previous studies misestimate the number of nonlocal HDDTs in the region and cannot identify the origin of the HDDTs on unmonitored road segments, resulting in unclear spatiotemporal distribution patterns of the emission contributions. This study therefore inferred the origins of HDDTs using trajectory data mining and obtained an accurate decomposition of the emission contribution from nonlocal HDDTs, from the single-vehicle-based HDDT emission inventory. A case study is conducted in Beijing. The results showed that the emission contribution of HDDTs from other regions to Beijing has a significant aggregation pattern and power function relationship with distance. The majority of nonlocal HDDTs in Beijing originate from Hebei and Tianjin and have the largest interaction intensity with Beijing, contributing 43% of the traffic counts and 37.05% of the emission intensity of HDDTs in Beijing. Temporally, the emission contribution of HDDTs from different regions has daily periodicity and is affected by major festivals. Nonlocal HDDTs dominate night-time emissions, which contributed 69.94% of total emissions at 1:00 a.m. The spatial heterogeneity of the emission contribution structure is mainly attributable to the traffic volume, highway freight ton-kilometers, and tertiary industry proportion. These findings extend the scientific understanding of the emission contributions of HDDTs and provide an important scientific basis for future strategies related to the control and management of emissions from HDDTs. [Display omitted] • The origin of heavy-duty diesel trucks is inferred based on trajectory mining. • A single-vehicle-based vehicle emission inventory is established. • Emission contribution structure of heavy-duty diesel trucks shows heterogeneity. • Emission contribution of heavy-duty diesel trucks has daily periodicity. [ABSTRACT FROM AUTHOR]

Abstract: Study of human activities in space and time has been an important research topic in transportation research. Limitations of conventional statistical methods for analysis of individual-level human activities have encouraged spatiotemporal analysis of human activity patterns in a space–time context. Based on Hägerstrand’s time geography, this study presents a space–time GIS approach that is capable of representing and analyzing spatiotemporal activity data at the individual level. Specifically, we have developed an ArcGIS extension, named Activity Pattern Analyst (APA), to facilitate exploratory analysis of activity diary data. This extension covers a set of functions such as space–time path generation, space–time path segmentation, space–time path filter, and activity distribution/density pattern exploration. It also provides a space–time path based multi-level clustering method to investigate individual-level spatiotemporal patterns. Using an activity diary dataset collected in Beijing, China, this paper presents how this Activity Pattern Analyst extension can facilitate exploratory analysis of individual activity diary data to uncover spatiotemporal patterns of individual activities. [Copyright &y& Elsevier]