The construction of noise maps is of great significance for the development of urban sustainability and the protection of residents’ physical and mental health. The traditional noise map construction method is difficult to be widely used because of its low update frequency and high drawing cost. Based on the crowd-sensing technology and Latent Factor Model (LFM), this paper proposes a new noise map completion method called Spatial-Temporally Related LFM (STR-LFM) for solving the problem of data sparseness. First, the geographic information features including Point of Interest (POI), road network and building outline are fully excavated, and then combine the correlation of the samples in the time dimension to construct the similarity matrixes. After that, use the k-nearest neighbor algorithm to find out the similar samples of missing positions, and finally regard their weighted fusion as the predicted values. Experimental results show that the recovery error is lower than other commonly used methods, and the proposed method has better stability when faced with data sparseness problems at different levels.

Quantifying methane (CH₄) emissions from paddy fields is essential for evaluating the environmental risks of the paddy rice production system, and improving the accuracy of CH₄ modeling is a key issue that needs to be addressed. Based on a database containing 835 field measurements, both single national and region-specific models were established to estimate CH₄ emissions from paddy fields considering different environmental factors and management patterns using 70% of the measurements. The remaining 30% of the measurements were then used for model evaluation. The performance of the region-specific model was better than that of the single national model. The region-specific model could simulate CH₄ emissions in an unbiased manner with R² values of 0.15–0.70 and efficiency values of 11–60%. The paddy rice type, water regime, organic amendment, latitude, and soil characteristics (pH and bulk density) were identified as the main drivers in the models. By inputting the high-resolution spatial data of these drivers into the established model, the CH₄ emissions from China’s paddy fields were estimated to be 4.75 Tg in 2015, with a 95% confidence interval of 4.19–5.61 Tg. The results indicated that establishing and driving a region-specific model with high-resolution data can improve the estimation accuracy of CH₄ emissions from paddy fields.

Mobile in situ concentration and meteorology data were collected for the Chino Dairy Complex in the Los Angeles Basin by AMOG (AutoMObile trace Gas) Surveyor on 25 June 2015 to characterize husbandry emissions in the near and far field in convoy mode with MISTIR (Mobile Infrared Sensor for Tactical Incident Response), a mobile upwards-looking, column remote sensing spectrometer. MISTIR reference flux validated AMOG plume inversions at different information levels including multiple gases, GoogleEarth imagery, and airborne trace gas remote sensing data. Long-term (9-yr.) Infrared Atmospheric Sounding Interferometer satellite data provided spatial and trace gas temporal context.For the Chino dairies, MISTIR-AMOG ammonia (NH₃) agreement was within 5% (15.7 versus 14.9 Gg yr⁻¹, respectively) using all information. Methane (CH₄) emissions were 30 Gg yr⁻¹ for a 45,200 herd size, indicating that Chino emission factors are greater than previously reported.Single dairy inversions were much less successful. AMOG-MISTIR agreement was 57% due to wind heterogeneity from downwind structures in these near-field measurements and emissions unsteadiness. AMOG CH₄, NH₃, and CO₂ emissions were 91, 209, and 8200 Mg yr⁻¹, implying 2480, 1870, and 1720 head using published emission factors. Plumes fingerprinting identified likely sources including manure storage, cowsheds, and a structure with likely natural gas combustion.NH₃ downwind of Chino showed a seasonal variation of a factor of ten, three times larger than literature suggests. Chino husbandry practices and trends in herd size and production were reviewed and unlikely to add seasonality. Higher emission seasonality was proposed as legacy soil emissions, the results of a century of husbandry, supported by airborne remote sensing data showing widespread emissions from neighborhoods that were dairies 15 years prior, and AMOG and MISTIR observations. Seasonal variations provide insights into the implications of global climate change and must be considered when comparing surveys from different seasons.

Long- and short-term exposure to PM2.5 is of great concern in China due to its adverse population health effects. Characteristic of the severity of the situation in China is that in the Jing-Jin-Ji region considered in this work a total of 2725 excess deaths have been attributed to short-term PM2.5 exposure during the period January 10–31, 2013. Technically, the processing of large space-time PM2.5 datasets and the mapping of the space-time distribution of PM2.5 concentrations often constitute high-cost projects. To address this situation, we propose a synthetic modeling framework based on the integration of (a) the Bayesian maximum entropy method that assimilates auxiliary information from land-use regression and artificial neural network (ANN) model outputs based on PM2.5 monitoring, satellite remote sensing data, land use and geographical records, with (b) a space-time projection technique that transforms the PM2.5 concentration values from the original spatiotemporal domain onto a spatial domain that moves along the direction of the PM2.5 velocity spread. An interesting methodological feature of the synthetic approach is that its components (methods or models) are complementary, i.e., one component can compensate for the occasional limitations of another component. Insight is gained in terms of a PM2.5 case study covering the severe haze Jing-Jin-Ji region during October 1–31, 2015. The proposed synthetic approach explicitly accounted for physical space-time dependencies of the PM2.5 distribution. Moreover, the assimilation of auxiliary information and the dimensionality reduction achieved by the synthetic approach produced rather impressive results: It generated PM2.5 concentration maps with low estimation uncertainty (even at counties and villages far away from the monitoring stations, whereas during the haze periods the uncertainty reduction was over 50% compared to standard PM2.5 mapping techniques); and it also proved to be computationally very efficient (the reduction in computational time was over 20% compared to standard mapping techniques).

Artificial lighting at night has becoming a new type of pollution posing an important anthropogenic environmental pressure on organisms. The objective of this research was to examine the potential association between nighttime artificial light pollution and nest densities of the three main sea turtle species along Florida beaches, including green turtles, loggerheads, and leatherbacks. Sea turtle survey data was obtained from the “Florida Statewide Nesting Beach Survey program”. We used the new generation of satellite sensor “Visible Infrared Imaging Radiometer Suite (VIIRS)” (version 1 D/N Band) nighttime annual average radiance composite image data. We defined light pollution as artificial light brightness greater than 10% of the natural sky brightness above 45° of elevation (>1.14 × 10⁻¹¹ Wm⁻²sr⁻¹). We fitted a generalized linear model (GLM), a GLM with eigenvectors spatial filtering (GLM-ESF), and a generalized estimating equations (GEE) approach for each species to examine the potential correlation of nest density with light pollution. Our models are robust and reliable in terms of the ability to deal with data distribution and spatial autocorrelation (SA) issues violating model assumptions. All three models found that nest density is significantly negatively correlated with light pollution for each sea turtle species: the higher light pollution, the lower nest density. The two spatially extended models (GLM-ESF and GEE) show that light pollution influences nest density in a descending order from green turtles, to loggerheads, and then to leatherbacks. The research findings have an implication for sea turtle conservation policy and ordinance making. Near-coastal lights-out ordinances and other approaches to shield lights can protect sea turtles and their nests. The VIIRS DNB light data, having significant improvements over comparable data by its predecessor, the DMSP-OLS, shows promise for continued and improved research about ecological effects of artificial light pollution.

Understanding the effects of urban morphology on urban environmental noise (UEN) at a regional scale is crucial for creating a pleasant urban acoustic environment. This study seeks to investigate how the urban morphology influences the UEN in the Shenzhen metropolitan region of China, by employing remote sensing and geographic information data. The UEN in this study consists of not only regional environmental noise (RN), but also traffic noise (TN). The experimental results reveal the following findings: 1) RN is positively correlated with the nighttime light intensity (NTL) and land surface temperature (LST) (p < 0.05). More interestingly, landscape composition and configuration can also significantly affect RN. For instance, urban vegetation can mitigate the RN (r = −0.411, p < 0.01). There is a reduced RN effect when fewer buildings exist in an urban landscape, in terms of the positive relationship between building density and RN (r = 0.188, p < 0.01). Given the same percentage of building area, buildings are more effective at reducing noise when they are distributed across the urban scenes, rather than being spatially concentrated (r = −0.205, p < 0.01). 2) TN positively relates to large (r = 0.520, p < 0.01) and small–medium (r = 0.508, p < 0.01) vehicle flow. In addition, vegetation along or near roads can alleviate the TN effect (r = −0.342, p < 0.01). TN can also become more severe in urban landscapes where there is higher road density (r = 0.307, p < 0.01). 3) Concerning the urban functional zones, traffic land is the greatest contributor to urban RN, followed by mixed residential and commercial land. The findings revealed by this research will indicate how to mitigate UEN.

Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0–0.10 m, or 0–0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km², with a median of 0.4 samples per km². The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis (PCA) and cluster analysis (CA).

Atmospheric nitrogen dioxide (NO₂) is an important reactive gas pollutant harmful to human health. The spatiotemporal coverage provided by traditional NO₂ monitoring methods is insufficient, especially in the suburban and rural areas of north China, which have a high population density and experience severe air pollution. In this study, we implemented a spatiotemporal neural network (STNN) model to estimate surface NO₂ from multiple sources of information, which included satellite and in situ measurements as well as meteorological and geographical data. The STNN predicted NO₂ with high accuracy, with a coefficient of determination (R²) of 0.89 and a root mean squared error of 5.8 μg/m³ for sample-based 10-fold cross-validation. Based on the surface NO₂ concentration determined by the STNN, we analyzed the spatial distribution and temporal trends of NO₂ pollution in north China. We found substantial drops in surface NO₂ concentrations ranging between 9.1% and 33.2% for large cities during the 2020 COVID-19 lockdown when compared to those in 2019. Moreover, we estimated the all-cause deaths attributed to NO₂ exposure at a high spatial resolution of about 1 km, with totals of 6082, 4200, and 18,210 for Beijing, Tianjin, and Hebei Provinces in 2020, respectively. We observed remarkable regional differences in the health impacts due to NO₂ among urban, suburban, and rural areas. Generally, the STNN model could incorporate spatiotemporal neighboring information and infer surface NO₂ concentration with full coverage and high accuracy. Compared with machine learning regression techniques, STNN can effectively avoid model overfitting and simultaneously consider both spatial and temporal correlations of input variables using deep convolutional networks with residual blocks. The use of the proposed STNN model, as well as the surface NO₂ dataset, can benefit air quality monitoring, forecasting, and health burden assessments.

Recently, the frequent occurrence of haze with aerosol pollution in China has attracted worldwide attention. Air pollutant emissions in conjunction with changing meteorological conditions create environment pollution in China. Aerosol pollution is spatially centralized in four regions of China, including the North China Plain, Yangtze River Delta, Pearl River Delta, and Sichuan Basin. In this observational study, a new center of aerosol pollution was identified in the Twain-Hu Basin (THB), covering the Hubei and Hunan provinces in central China. Based on the analysis of 19 years of satellite remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS), the THB experiences high aerosol optical depth (AOD) values exceeding 0.9. The fine mode fraction (FMF) values below 0.3 were also detected over the aerosol polluted THB region, where aerosol pollution was dominated by the mixed aerosol type. This reflects the role of intense human activities and the unique aerosol processes involved in the regional aerosol pollution over central China. The interannual AOD variations for THB present an increasing trend (mostly >0.02 yr⁻¹) between 2000 and 2011 and a significant descending trend (mostly < -0.06 yr⁻¹) between 2011 and 2018. This inverse trends in AOD with an overall increasing trend in FMF characterizes the past 19 years. This highlights the contribution of the increase in submicron particles and meteorological effects to the regional aerosol concentrations during recent years when considering the reduced anthropogenic aerosol emissions in the THB.

Adverse effects of ambient fine particles (PM₂.₅) on sperm quality and oocyte fertilization have been identified by previous research. However, insufficient human studies tested associations between PM₂.₅ and decreased fertility rates.We associated long-term exposure to PM₂.₅ and county-level fertility rates reported by 2010 census across China. Exposure assessments were based on PM₂.₅ maps (2009–2010) with a spatial resolution of 0.1° derived from satellite remote sensing data from another published study. We used a Poisson regression to examine the relationship between PM₂.₅ and fertility rates with adjustment of potential confounders including county-level socioeconomic factors (e.g. sex ratio) and a spatially smoothed trend.We found that fertility rates were significantly decreased by 2.0% (95% confidence interval: 1.8%, 2.1%) per 10 μg/m³ increment of PM₂.₅. We also found a geographical variation of the associations.The study add to epidemiological evidences on adverse effects of PM₂.₅ on fertility rates.