Interaction between Extreme Temperature Events and Fine Particulate Matter on Cardiometabolic Multimorbidity: Evidence from Four National Cohort Studies.

Accumulating evidence linked extreme temperature events (ETEs) and fine particulate matter (PM2.5) to cardiometabolic multimorbidity (CMM); however, it remained unknown if and how ETEs and PM2.5 interact to trigger CMM occurrence. Merging four Chinese national cohorts with 64,140 free-CMM adults, we provided strong evidence among ETEs, PM2.5 exposure, and CMM occurrence. Performing Cox hazards regression models along with additive interaction analyses, we found that the hazards ratio (HRs) of CMM occurrence associated with heatwave and cold spell were 1.006-1.019 and 1.063-1.091, respectively. Each 10 µg/m3 increment of PM2.5 concentration was associated with 17.9% (95% confidence interval: 13.9-22.0%) increased risk of CMM. Similar adverse effects were also found among PM2.5 constituents of nitrate, organic matter, sulfate, ammonium, and black carbon. We observed a synergetic interaction of heatwave and PM2.5 pollution on CMM occurrence with relative excess risk due to the interaction of 0.999 (0.663-1.334). Our study provides novel evidence that both ETEs and PM2.5 exposure were positively associated with CMM occurrence, and the heatwave interacts synergistically with PM2.5 to trigger CMM.

Effects of short- and long-term exposures to multiple air pollutants on depression among the labor force: A nationwide longitudinal study in China.

BACKGROUND: Depression prevalence has surged within the labor force population in recent years. While links between air pollutants and depression were explored, there was a notable scarcity of research focusing on the workforce. METHODS: This nationwide longitudinal study analyzed 27,457 workers aged 15-64. We estimated monthly mean concentrations of fine particulate matter (PM2.5), its primary components, and Ozone (O3) at participants' residences using spatiotemporal models. To assess the relationship between short- (1 to 3 months) and long-term (1 to 2 years) exposure to various air pollutants and depressive levels and occurrences, we employed linear mixed-effects models and mixed-effects logistic regression. We considered potential occupational moderators, such as labor contracts, overtime compensation, and total annual income. RESULTS: We found significant increases in depression risks within the workforce linked to both short- and long-term air pollution exposure. A 10 µg/m3 rise in 2-year average PM2.5, black carbon (BC), and O3 concentrations correlated with increments in depressive scores of 0.009, 0.173, and 0.010, and a higher likelihood of depression prevalence by 0.5 %, 12.6 %, and 0.7 %. The impacts of air pollutants and depression were more prominent in people without labor contracts, overtime compensation, and lower total incomes. CONCLUSION: Exposures to air pollutants could increase the risk of depression in the labor force population. The mitigating effects of higher income, benefits, and job security against depression underscore the need for focused mental health interventions.

Long-term exposure to ambient particulate matter is associated with prognosis in people living with HIV/AIDS: Evidence from a longitudinal study.

BACKGROUND: Evidence on the association between particulate matter (PM) exposure and prognosis in people living with HIV/AIDS (PWHA) is scarce. We aim to investigate the associations of long-term exposure to PM with AIDS-related deaths and complications. METHODS: We collected follow-up information on 7444 PWHAs from 2000 to 2021 from the HIV/AIDS Comprehensive Response Information Management System of the Wuhan Center for Disease Control and Prevention. The AIDS-related deaths and complications were assessed by physicians every 3 to 6 months, and the monthly average PM concentrations for each PWHA were extracted from the China High Air Pollutants dataset. We employed time-varying Cox regression models to evaluate the associations of the average cumulative PM exposure concentrations with AIDS-related deaths and complications, as well as the mediating effects of AIDS-related complications in PM-induced AIDS-related deaths. RESULTS: For each 1 µg/m3 increase in PM1, PM2.5, and PM10, the adjusted hazard ratios (HRs) for AIDS-related deaths were 1.021 (1.009, 1.033), 1.012 (1.005, 1.020), and 1.010 (1.005, 1.015), respectively; and the HRs for AIDS-related complications were 1.049 (1.034, 1.064), 1.029 (1.020, 1.038), and 1.031 (1.024, 1.037), respectively. AIDS-related complications mediated 18.38 % and 18.68 % of the association of exposure to PM1 and PM2.5 with AIDS-related deaths, respectively. The association of PM exposure with AIDS-related deaths was more significant in older PWHA. Meanwhile, the association between PM exposure and AIDS-related complications was stronger in PWHA with a BMI ≥ 24 kg/m2. CONCLUSION: Long-term exposure to PM is positively associated with AIDS-related deaths and complications, and AIDS-related complications have mediating effects in PM-induced AIDS-related deaths. Our evidence emphasizes that enhanced protection against PM exposure for PWHAs is an additional mitigation strategy to reduce AIDS-related deaths and complications.

Ambient ozone pollution impairs glucose homeostasis and contributes to renal function decline: Population-based evidence.

Particulate matter pollution could increase the risk of kidney disease, while evidence for ozone exposure is less well-established. Here, we aimed to evaluate the effect of ozone pollution on renal function and explore mechanisms. We first conducted a cross-sectional study based on Wuhan Chronic Disease Cohort Study baseline information. We recruited 2699 eligible participants, estimated their residential ozone concentrations, collected fasting peripheral blood samples for biochemical analysis and calculated the estimated glomerular filtration rate (eGFR). The linear regression model was applied to evaluate the long-term association between ozone pollution and eGFR. Then, we recruited another 70 volunteers as a panel with 8 rounds follow-up visits. We calculated the eGFR and measured fasting blood glucose and lipid levels. The linear mixed-effect model along with mediation analysis were performed to confirm the short-term association and explore potential mechanisms, respectively. For the long-term association, a 10.95 µg/m3 increment of 3-year ozone exposure was associated with 2.96 mL/min/1.73 m2 decrease in eGFR (95%CI: -4.85, -1.06). Furthermore, the drinkers exhibited a pronounced declination of eGFR (-7.46 mL/min/1.73 m2, 95%CI: -11.84, -3.08) compared to non-drinkers in relation to ozone exposure. Additionally, a 19.02 µg/m3 increase in 3-day ozone concentrations was related to 2.51 mL/min/1.73 m2 decrease in eGFR (95%CI: -3.78, -1.26). Hyperglycemia and insulin resistance mediated 12.2% and 16.5% of the aforementioned association, respectively. Our findings indicated that higher ozone pollution could affect renal function, and the hyperglycemia and insulin resistance linked to ozone might be the underlying mechanisms.

Spectral Response Function-Guided Deep Optimization-Driven Network for Spectral Super-Resolution.

Hyperspectral images (HSIs) are crucial for many research works. Spectral super-resolution (SSR) is a method used to obtain high-spatial-resolution (HR) HSIs from HR multispectral images. Traditional SSR methods include model-driven algorithms and deep learning. By unfolding a variational method, this article proposes an optimization-driven convolutional neural network (CNN) with a deep spatial-spectral prior, resulting in physically interpretable networks. Unlike the fully data-driven CNN, auxiliary spectral response function (SRF) is utilized to guide CNNs to group the bands with spectral relevance. In addition, the channel attention module (CAM) and the reformulated spectral angle mapper loss function are applied to achieve an effective reconstruction model. Finally, experiments on two types of data sets, including natural and remote sensing images, demonstrate the spectral enhancement effect of the proposed method, and also, the classification results on the remote sensing data set verified the validity of the information enhanced by the proposed method.

The Effects of Fireworks Discharge on Atmospheric PM2.5 Concentration in the Chinese Lunar New Year.

Discharging fireworks during the Chinese Lunar New Year celebrations is a deep-rooted custom in China. In this paper, we analyze the effect of this cultural activity on PM2.5 concentration using both ground observations and satellite data. By combining remote sensing data, the problem of uneven spatial distribution of ground monitoring has been compensated, and the research time span has been expanded. The results show that the extensive firework displays on New Year's Eve lead to a remarkable increase in nationwide PM2.5 concentration, which were 159~223% of the average level, indicating the instantaneous effect far exceeds that of any other factor over the whole year. However, the averaged PM2.5 concentrations of the celebration period were 0.99~16.32 µg/m3 lower compared to the average values of the corresponding pre-celebration period and post-celebration period, indicating the sustained effect is not very significant. The implementation of firework prohibition policies can greatly reduce the instantaneous PM2.5 increase, but no obvious air quality improvement is observed over the entire celebration period. Combining these findings and the cultural significance of this activity, we recommend that this custom is actively maintained, using new technologies and scientific governance programs to minimize the negative effects.

Temporal and Spatial Features of the Correlation between PM2.5 and O3 Concentrations in China.

In recent years, particulate matter of 2.5 µm or less (PM2.5) pollution in China has decreased but, at the same time, ozone (O3) pollution has become increasingly serious. Due to the different research areas and research periods, the existing analyses of the correlation between PM2.5 and O3 have reached different conclusions. In order to clarify the relationship between PM2.5 and O3, this study selected mainland China as the research area, based on the PM2.5 and O3 concentration data of 1458 air quality monitoring stations, and analyzed the correlation between PM2.5 and O3 for different time scales and geographic divisions. Moreover, by combining the characteristics of the pollutants, topography, and climatic features of the study area, we attempted to discuss the causes of the spatial and temporal differences of R-PO (the correlation between PM2.5 and O3). The study found that: (1) R-PO tends to show a positive correlation in summer and a negative correlation in winter, (2) the correlation coefficient of PM2.5 and O3 is lower in the morning and higher in the afternoon, and (3) R-PO also shows significant spatial differences, including north-south differences and coastland-inland differences.

Integration of Remote Sensing and Social Sensing Data in a Deep Learning Framework for Hourly Urban PM2.5 Mapping.

Fine spatiotemporal mapping of PM2.5 concentration in urban areas is of great significance in epidemiologic research. However, both the diversity and the complex nonlinear relationships of PM2.5 influencing factors pose challenges for accurate mapping. To address these issues, we innovatively combined social sensing data with remote sensing data and other auxiliary variables, which can bring both natural and social factors into the modeling; meanwhile, we used a deep learning method to learn the nonlinear relationships. The geospatial analysis methods were applied to realize effective feature extraction of the social sensing data and a grid matching process was carried out to integrate the spatiotemporal multi-source heterogeneous data. Based on this research strategy, we finally generated hourly PM2.5 concentration data at a spatial resolution of 0.01°. This method was successfully applied to the central urban area of Wuhan in China, which the optimal result of the 10-fold cross-validation R2 was 0.832. Our work indicated that the real-time check-in and traffic index variables can improve both quantitative and mapping results. The mapping results could be potentially applied for urban environmental monitoring, pollution exposure assessment, and health risk research.

The influence of urban planning factors on PM2.5 pollution exposure and implications: A case study in China based on remote sensing, LBS, and GIS data.

In recent years, haze pollution has become a serious environmental problem affecting cities in China. Reducing PM2.5 concentrations through urban planning is a promising method that has been a focus of recent multidisciplinary research. Most existing studies only analyze the relationship between urban planning factors and PM2.5 concentration, and it is difficult to accurately reflect residents' actual air pollution exposure without considering their space-time behaviors. This study uses satellite remote sensing and location service data to measure PM2.5 pollution exposure in Wuhan metropolitan area and explores the effects of urban spatial structure, land use, spatial form, transportation, and green space on pollution exposure. The results show that spatial structure, building density, road density, and green space coverage have a significant impact on PM2.5 pollution exposure. In addition, this study proposes corresponding implications for urban planning to improve public respiratory health.

The relationships between PM2.5 and aerosol optical depth (AOD) in mainland China: About and behind the spatio-temporal variations.

Satellite aerosol products have been widely used to retrieve ground PM2.5 concentration because of their wide coverage and continuous spatial distribution. While more and more studies have focused on the retrieval algorithms, the foundation for the retrieval-relationship between PM2.5 and satellite aerosol optical depth (AOD) has not been fully investigated. In this study, the relationships between PM2.5 and AOD were investigated in 368 cities in mainland China from February 2013 to December 2017, at different temporal and regional scales. Pearson correlation coefficients and the PM2.5/AOD ratio were used as indicators. Firstly, we established the relationship between PM2.5 and AOD in terms of the spatio-temporal variations, and discuss the impact of some potential factors for a better understanding of the spatio-temporal variations. Spatially, we found that the correlation is higher in the Beijing-Tianjin-Hebei and Chengyu regions and weaker in coastal areas. The PM2.5/AOD ratio shows an obvious north-south difference, with the ratio in North China higher than South China. Temporally, the correlation coefficient tends to be higher in May and September, with the PM2.5/AOD ratio higher in winter and lower in summer. As for interannual variations, we detected a decreasing tendency for the PM2.5-AOD correlation and PM2.5/AOD ratio for recent years. Then, to determine the impact of the weakening of the PM2.5-AOD relationship on PM2.5 remote sensing retrieval performance, a geographically weighted regression (GWR) retrieval experiment was conducted. The results showed that the performance of retrievals is also decreasing while PM2.5-AOD relationship getting weaker. Our study investigated the PM2.5-AOD relationship over a large extent at the city scale, and investigated the temporal variations in terms of interannual variations. The results will be useful for the satellite retrieval of PM2.5 concentration and will help us to further understand the PM2.5 pollution situation in mainland China.

A long-term and comprehensive assessment of the urbanization-induced impacts on vegetation net primary productivity.

Urbanization not only directly alters the regional ecosystem net primary productivity (NPP) through land-cover replacement, but it is also accompanied by huge indirect impacts due to the associated climate change and anthropogenic activities. However, to date, limited efforts have been made to quantitatively separate the two types of urbanization impacts, and the continuous variations over a long-time span are not well understood. In this study, both the long-term direct and indirect impacts of urbanization on NPP were established and analyzed based on multi-source remote sensing data, taking the city of Kunming in China as a case study area. The results indicated that the intense urbanization process has led to a continuous decrease in NPP from 1990 to 2014, due to the direct impact of land-cover replacement. Nevertheless, the urbanization has also resulted in an apparently positive indirect impact on NPP, which has offset about 30% of the direct impact in recent years. The increasing trend of the indirect impact was found to be higher than the NPP trend in the surrounding forest areas, which proves that vegetation growth has been promoted by the urban environment. The indirect impact has also shown great spatial and temporal heterogeneity, with generally higher values in the old city area and winter season. This can mostly be attributed to the distribution of temperature, i.e., the urban heat island effect, which has shown a significantly positive correlation with the indirect impact. However, the correlations between NPP and climatic factors were found to be completely different, which confirmed the need to separate the direct and indirect impacts. Overall, this study has demonstrated that urbanization has reduced the total NPP over the region, but has promoted some vegetation growth, and the knowledge of the indirect impact will help to support urban greening planning.

Estimation of spatiotemporal PM1.0 distributions in China by combining PM2.5 observations with satellite aerosol optical depth.

Particulates smaller than 1.0 µm (PM1.0) have strong associations with public health and environment, and considerable exposure data should be obtained to understand the actual environmental burden. This study presented a PM1.0 estimation strategy based on the generalised regression neural network model. The proposed strategy combined ground-based observations of PM2.5 and satellite-derived aerosol optical depth (AOD) to estimate PM1.0 concentrations in China from July 2015 to June 2017. Results indicated that the PM1.0 estimates agreed well with the ground-based measurements with an R2 of 0.74, root mean square error of 19.0 µg/m3 and mean absolute error of 11.4 µg/m3 as calculated with the tenfold cross-validation method. The diurnal estimation performance displayed remarkable single-peak variation with the highest R2 of 0.80 at noon, and the seasonal estimation performance showed that the proposed method could effectively capture high-pollution events of PM1.0 in winter. Spatially, the most polluted areas were clustered in the North China Plain, where the average estimates presented a bimodal distribution during daytime. In addition, the quality of satellite-derived AOD, the robustness of the interpolation algorithm and the proportion of PM1.0 in PM2.5 were confirmed to affect the estimation accuracy of the proposed model.

Assessment of the impact of spatial heterogeneity on microwave satellite soil moisture periodic error.

An accurate temporal and spatial characterization of errors is required for the efficient processing, evaluation, and assimilation of remotely-sensed surface soil moisture retrievals. However, empirical evidence exists that passive microwave soil moisture retrievals are prone to periodic artifacts which may complicate their application in data assimilation systems (which commonly treat observational errors as being temporally white). In this paper, the link between such temporally-periodic errors and spatial land surface heterogeneity is examined. Both the synthetic experiment and site-specified cases reveal that, when combined with strong spatial heterogeneity, temporal periodicity in satellite sampling patterns (associated with exact repeat intervals of the polar-orbiting satellites) can lead to spurious high frequency spectral peaks in soil moisture retrievals. In addition, the global distribution of the most prominent and consistent 8-day spectral peak in the Advanced Microwave Scanning Radiometer - Earth Observing System soil moisture retrievals is revealed via a peak detection method. Three spatial heterogeneity indicators - based on microwave brightness temperature, land cover types, and long-term averaged vegetation index - are proposed to characterize the degree to which the variability of land surface is capable of inducing periodic error into satellite-based soil moisture retrievals. Regions demonstrating 8-day periodic errors are generally consistent with those exhibiting relatively higher heterogeneity indicators. This implies a causal relationship between spatial land surface heterogeneity and temporal periodic error in remotely-sensed surface soil moisture retrievals.

The Relationships between PM2.5 and Meteorological Factors in China: Seasonal and Regional Variations.

The interactions between PM2.5 and meteorological factors play a crucial role in air pollution analysis. However, previous studies that have researched the relationships between PM2.5 concentration and meteorological conditions have been mainly confined to a certain city or district, and the correlation over the whole of China remains unclear. Whether spatial and seasonal variations exist deserves further research. In this study, the relationships between PM2.5 concentration and meteorological factors were investigated in 68 major cities in China for a continuous period of 22 months from February 2013 to November 2014, at season, year, city, and regional scales, and the spatial and seasonal variations were analyzed. The meteorological factors were relative humidity (RH), temperature (TEM), wind speed (WS), and surface pressure (PS). We found that spatial and seasonal variations of their relationships with PM2.5 exist. Spatially, RH is positively correlated with PM2.5 concentration in north China and Urumqi, but the relationship turns to negative in other areas of China. WS is negatively correlated with PM2.5 everywhere except for Hainan Island. PS has a strong positive relationship with PM2.5 concentration in northeast China and mid-south China, and in other areas the correlation is weak. Seasonally, the positive correlation between PM2.5 concentration and RH is stronger in winter and spring. TEM has a negative relationship with PM2.5 in autumn and the opposite in winter. PS is more positively correlated with PM2.5 in autumn than in other seasons. Our study investigated the relationships between PM2.5 and meteorological factors in terms of spatial and seasonal variations, and the conclusions about the relationships between PM2.5 and meteorological factors are more comprehensive and precise than before. We suggest that the variations could be considered in PM2.5 concentration prediction and haze control to improve the prediction accuracy and policy efficiency.

Multiple timescale analysis of the urban heat island effect based on the Community Land Model: a case study of the city of Xi'an, China.

Urban heat islands (UHIs) are the phenomenon of urban regions usually being warmer than rural regions, which significantly impacts both the regional ecosystem and societal activities. Numerical simulation can provide spatially and temporally continuous datasets for UHI analysis. In this study, a spatially and temporally continuous ground temperature dataset of Xi'an, China was obtained through numerical simulation based on the Community Land Model version 4.5 (CLM4.5), at a temporal resolution of 30 min and a spatial resolution of 0.05∘× 0.05∘. Based on the ground temperature, the seasonal average UHI intensity (UHII) was calculated and the seasonal variation of the UHI effect was analyzed. The monthly variation tendency of the urban heat stress was also investigated. Based on the diurnal cycle of ground temperature and the UHI effect in each season, the variation tendencies of the maximum, minimum, and average UHII were analyzed. The results show that the urban heat stress in summer is the strongest among all four seasons. The heat stress in urban areas is very significant in July, and the UHII is the weakest in January. Regarding the diurnal cycle of UHII, the maximum always appears at 06:30 UTC to 07:30 UTC, while the minimum intensity of the UHI effect occurs at different times in the different seasons. The results of this study could provide a reference for policymakers about how to reduce the damage caused by heat stress.

Pansharpening with a Guided Filter Based on Three-Layer Decomposition.

State-of-the-art pansharpening methods generally inject the spatial structures of a high spatial resolution (HR) panchromatic (PAN) image into the corresponding low spatial resolution (LR) multispectral (MS) image by an injection model. In this paper, a novel pansharpening method with an edge-preserving guided filter based on three-layer decomposition is proposed. In the proposed method, the PAN image is decomposed into three layers: A strong edge layer, a detail layer, and a low-frequency layer. The edge layer and detail layer are then injected into the MS image by a proportional injection model. In addition, two new quantitative evaluation indices, including the modified correlation coefficient (MCC) and the modified universal image quality index (MUIQI) are developed. The proposed method was tested and verified by IKONOS, QuickBird, and Gaofen (GF)-1 satellite images, and it was compared with several of state-of-the-art pansharpening methods from both qualitative and quantitative aspects. The experimental results confirm the superiority of the proposed method.

Adaptive Norm Selection for Regularized Image Restoration and Super-Resolution.

In the commonly employed regularization models of image restoration and super-resolution (SR), the norm determination is often challenging. This paper proposes a method to adaptively determine the optimal norms for both fidelity term and regularization term in the (SR) restoration model. Inspired by a generalized likelihood ratio test, a piecewise function is proposed to solve the norm of the fidelity term. This function can find the stable norm value in a certain number of iterations, regardless of whether the noise type is Gaussian, impulse, or mixed. For the regularization norm, the main advantage of the proposed method is that it is locally adaptive. Specifically, it assigns different norms for different pixel locations, according to the local activity measured by a structure tensor metric. The proposed method was tested using different types of images. The experimental results and error analyses verify the efficacy of the method.

[Maximum a posteriori fusion method based on gradient consistency constraint for multispectral/panchromatic remote sensing images].

Multispectral (MS) images with high spatial resolution (HR) can be obtained by fusing MS images and panchromatic (PAN) image, the HR MS images have an important significance in image interpretation and classification, etc. In the present paper, a new image fusion method based on gradient consistency constraint for MS/PAN images is developed. The method is based on maximum a posteriori (MAP) framework. The relationship of desired HR MS images and PAN image is formulated by gradient consistency constraint. Observation model of MS images and the Huber-Markov priori are combined to solve the fused image by gradient descent algorithm. In the proposed method, gradient consistency constraint is introduced, and defect of band number restriction is overcomed in conventional model-based fusion methods. Iterative step for every band is solved adaptively, and spectral characteristics of each band are fully taken into account, so it not only ensures the spectral information fidelity, but also improves the integration degree of spatial information of fused image. The proposed method has been tested using IKONOS and WorldView-2 images. It is compared with GS, AIHS and AMBF fusion methods from both qualitative and quantitative aspects. Experimental results show that the proposed method can better preserve spectral information while enhance spatial resolution, and it has broader applicability and better fusion result than other methods.

Regional spatially adaptive total variation super-resolution with spatial information filtering and clustering.

Total variation is used as a popular and effective image prior model in the regularization-based image processing fields. However, as the total variation model favors a piecewise constant solution, the processing result under high noise intensity in the flat regions of the image is often poor, and some pseudoedges are produced. In this paper, we develop a regional spatially adaptive total variation model. Initially, the spatial information is extracted based on each pixel, and then two filtering processes are added to suppress the effect of pseudoedges. In addition, the spatial information weight is constructed and classified with k-means clustering, and the regularization strength in each region is controlled by the clustering center value. The experimental results, on both simulated and real datasets, show that the proposed approach can effectively reduce the pseudoedges of the total variation regularization in the flat regions, and maintain the partial smoothness of the high-resolution image. More importantly, compared with the traditional pixel-based spatial information adaptive approach, the proposed region-based spatial information adaptive total variation model can better avoid the effect of noise on the spatial information extraction, and maintains robustness with changes in the noise intensity in the super-resolution process.

Adjustable model-based fusion method for multispectral and panchromatic images.

In this paper, an adjustable model-based image fusion method for multispectral (MS) and panchromatic (PAN) images is developed. The relationships of the desired high spatial resolution (HR) MS images to the observed low-spatial-resolution MS images and HR PAN image are formulated with image observation models. The maximum a posteriori framework is employed to describe the inverse problem of image fusion. By choosing particular probability density functions, the fused HR MS images are solved using a gradient descent algorithm. In particular, two functions are defined to adaptively determine most regularization parameters using the partially fused results at each iteration, retaining one parameter to adjust the tradeoff between the enhancement of spatial information and the maintenance of spectral information. The proposed method has been tested using QuickBird and IKONOS images and compared to several known fusion methods using quantitative evaluation indices. The experimental results verify the efficacy of this method.