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Air pollution has emerged as one of the most urgent problems worldwide over the past few years. Especially, fine particulate matter with a diameter smaller than 2.5 micrometres (PM2.5) has seriously endangered the human life and health worldwide. Thus, monitoring accurate and spatio-temporal continuously PM2.5 concentrations is essential for controlling air pollution. The key to accurately estimating PM2.5 concentrations is to build a reliable aerosol optical depth (AOD)–PM2.5 model, which is mainly determined by the AOD remotely sensed products, meteorological parameters, and estimating methods. However, no systematic evaluation and analysis of the influence of these three variables on AOD–PM2.5 model has yet been reported. Based on Terra and Aqua Moderate Resolution Imaging Spectroradiometer AOD products, reanalysis meteorological data, and ground-based PM2.5 data during 2014–2015 in eastern China, we analyzed the influence of above mentioned variables on the accuracy of the AOD–PM2.5 model. The four modeling methods investigated are stepwise multiple linear regression (SMLR), backpropagation neural network (BPNN), classification and regression tree (CART), and random forest (RF). Adjusted R2, mean absolute error (MAE), product index (PI), spatial resolution index (SRI), and Taylor diagrams are used to assess the accuracy of models. The results show that adding meteorological elements that vary with time and height can improve the accuracy by 4.97% over eastern China. The average PI of Terra AOD products increased by 0.15 compared with that of Aqua AOD products. Most SRIs have positive values, suggesting that AOD products with a spatial resolution of 10 km can lead to a more accurate AOD–PM2.5 model. The Taylor diagram indicates that RF model > BPNN model > CART model > SMLR model in terms of performance. Therefore, Terra AOD products with spatial resolution of 10 km and RF are more suitable for use with the AOD–PM2.5 model in eastern China.

Accurate spatial distribution information on gross domestic product (GDP) is of great importance for the analysis of economic development, industrial distribution and urbanization processes. Traditional administrative unit-based GDP statistics cannot depict the detailed spatial differences in GDP within each administrative unit. This paper presents a study of GDP spatialization in Ningbo City, China based on National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light (NTL) data and town-level GDP statistical data. The Landsat image, land cover, road network and topographic data were also employed as auxiliary data to derive independent variables for GDP modelling. Multivariate linear regression (MLR) and random forest (RF) regression were used to estimate GDP at the town scale and were assessed by cross-validation. The results show that the RF model achieved significantly higher accuracy, with a mean absolute error (MAE) of 109.46 million China Yuan (CNY)·km−2 and a determinate coefficient (R2 = 0.77) than the MLR model (MAE = 161.8 million CNY·km−2, R2 = 0.59). Meanwhile, by comparing with the estimated GDP data at the county level, the town-level estimated data showed a better performance in mapping GDP distribution (MAE decreased from 115.1 million CNY·km−2 to 74.8 million CNY·km−2). Among all of the independent variables, NTL, land surface temperature (Ts) and plot ratio (PR) showed higher impacts on the GDP estimation accuracy than the other variables. The GDP density map generated by the RF model depicted the detailed spatial distribution of the economy in Ningbo City. By interpreting the spatial distribution of the GDP, we found that the GDP of Ningbo was high in the northeast and low in the southwest and formed continuous clusters in the north. In addition, the GDP of Ningbo also gradually decreased from the urban centre to its surrounding areas. The produced GDP map provides a good reference for the future urban planning and socio-economic development strategies.

Urbanization has led to an obvious urban heat island (UHI) effect in the Yangtze River Delta (YRD), China. The ozone (O3) pollution in the YRD is getting worse. The UHI effect is a key factor that affects the O3 level. Understanding the influences of the UHI effect on O3 concentrations is necessary for improving air quality. In this study, the temporal and spatial relationship between UHI and O3 in the YRD during 2015 was investigated. The influence factors of UHI effect and O3 are both natural and artificial. Multi-source remote sensing data, which include land cover, land surface temperature (LST), Normalization Difference Vegetation Index (NDVI), and digital elevation model (DEM) data, were used to extract surface landscape elements. The results showed that: (1) the average hourly O3 concentration was 61.83 μg/m3 (30.92 ppb), the highest value was 105.32 μg/m3 (52.66 ppb) at 15:00 and the O3 peak was 82.50 μg/m3 (41.25 ppb) in September. The O3 concentrations and temperature have a similar variation trend both in diurnal and monthly. The O3 concentrations in coastal stations are higher than those inland. (2) The average daytime UHI intensity was 1.24 °C, and the daytime O3 concentration was 80.66 μg/m3 (40.33 ppb). There is a positive relationship between UHI and O3 in the YRD. The relationship in the central developed cities is higher than that in the northern and southern cities. (3) The related factors influencing UHI and O3 include surface landscape, topography and population. The LST and NDVI are most important among these factors. (4) Due to various geographical backgrounds, the UHI intensities and O3 concentrations show obvious spatial differences. This study provides a reference with which to better understand the relationship among UHI, O3 and related factors. Furthermore, the issues of atmospheric and energy transmission in this region deserve further study.

Aerosol can induce visibility reduction, affect radiation balance and modify cloud property on the environmental effect, and show the harmful effects on human health. Insight of aerosol becomes an integral task in the process of control measures for environmental pollution. The present study provided an analysis of temporal–spatial variations of aerosol optical depth (AOD) using the MOD04 level-2 in collection 6 (C6) with the deep blue retrieval algorithm from January 2005 to December 2015 over Yangtze River Delta (YRD) in China. The AOD validations between MODIS and Aerosol Robotic Network (AERONET) were estimated by the methods of regression, correlation. Then, the periodic features and trends of AOD and angstrom exponent (AE) were explored with the wavelet transformation (WT) procedure. Further, the variations of AOD and AE spatial distribution on multi-time scales (annual, monthly and season) were demonstrated. Meantime, the sources of AOD are discussed. It was found that the daily AOD from MODIS has a strong correlation relationship (slope = 0.9838, r = 0.84) with AERONET over YRD. The variations of both AOD and AE on time series have been distinct temporal periodic (12, 6 and 4 months) characteristics, and show the decreasing trends on annual and semi-annual periods. On annual, the AOD on spatial distribution is slowly declining from the northwest towards the southeast, and the AE on spatial distribution is gradually decreasing from the northwest to the southeast and from the land to the coast. The variations both inter-annual AOD and AE on spatial distribution show the inverse trends, respectively. On monthly, the means of AOD range from minimum 0.46 in January to maximum 0.90 in July, and the variations of spatial distribution mainly occur in the north parts of Yangtze River and some scattered areas with high terrain and south coast. The means of AE range from minimum 1.13 in October to maximum 1.58 in April, and the variations of spatial distribution are mainly found in the south of Henan, the north of Jiangsu, the coast belt and the riverside of Yangtze River and the high terrain regions. On seasonality, the means of AOD reaches its maximum 0.68 in summer and minimum 0.50 in winter, and the variations of spatial distribution mainly occur in the coast belt, the north parts of Hongze Lake and the south parts with high terrain. The means of AE reaches its maximum 1.48 in spring and minimum 1.25 in autumn, and the variations of spatial distribution were shown the similarity with that of monthly.

The relationship between urban heat island (UHI) and air pollution is worth exploring. We used land surface temperature (LST) and air quality data investigated the spatio-temporal distribution of UHI intensity (UHII) and six air pollutants of carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), fine particle matter (PM2.5), coarse particle matter (PM10) and sulfur dioxide (SO2) in the YRD during 2015–2019, and the correlation relationship between UHII and air pollutants concentration. Using regression analysis to analyze the influence of natural and human factors on the UHI and air pollutants. We found that: (1) Annual average daytime UHII was 1.20 ℃ and northern higher than southern cities. Nighttime UHII was 0.35 ℃ and southern higher than northern. Annual average CO, NO2, O3, PM2.5, PM10 and SO2 concentrations were 0.83 mg/m3, 35.41 ug/m3, 63.34 ug/m3, 44.16 ug/m3, 73.46 ug/m3, 13.86 ug/m3, respectively. Air pollution in inland cities were worse than coastal cities. (2) A significant positive correlation between daytime UHII and O3 concentration, and a negative correlation with other pollutants. (3) The influence of LST is the closest factors, followed by vegetation coverage, geographic location, elevation and economic development intensity on UHII and air pollution.

The acceleration of the industrialization process in China has increased the demand for electricity and triggered a power-plant building boom, especially along China’s eastern coast, where the economy gets off early and enjoys a fast development. The thermal plumes, residual chlorine, nuclides and other pollutions produced by the thermal and nuclear power plants have exerted an impact on the coastal eco-environment. To monitor the thermal pollution from the power plants at Yueqing Bay on the eastern coast, in this research, the distribution of sea surface temperature (SST) surrounding the power plants is obtained by using the SST retrieval methods developed for Landsat Enhanced Thematic Mapper Plus (ETM+), HJ-1B infrared sensor (IRS) and Terra moderate resolution imaging spectroradiometer (MODIS) data. The comparison of the SST retrieval results before and after the operation of power plants indicates that the total area of sea waters that is impacted by the thermal discharge from the two power plants at Yueqing Bay is approximately 17.95 km2, with the highest SST rise of 4.5 °C appearing over the waters around the outlet of the Huaneng Yuhuan power plant on the eastern shore, whereas the highest SST rise around the Zheneng Yueqing power plant on the western shore reaches 3.8 °C. The intensity and scope of influence of the thermal discharge mainly depend on the installed capacity of power plants, coastal terrain, and tide. Although the area where the SST rise is more than 3 °C is not large, thermal discharge still has an impact on bay ecosystems due to the relatively closed nature of the bay environment. Due to the influence of rising water temperatures on the reproduction and individual evolution of fish, shrimp, crabs, shellfish and other aquatic creatures, in the long term, the thermal pollution from coastal power plants will affect the volume of natural fishery and biological resources throughout the waters. The quantitative retrieval results also suggest that relative to MODIS data, Landsat ETM+ and HJ-1B IRS data with a high spatial resolution are more applicable to the estimation of small-scale SST, and IRS data with a high temporal resolution are more helpful in the study of spatio-temporal variability of thermal plumes from coastal power plants.

As an indispensable homestead, the ecosystem witnesses a large number of human activities. Undoubtedly, socio-economic development consumes a variety of natural resources and causes stress to the ecosystem. Only when the ecosystem is in under-loading, socio-economic development can be sustainable. In this paper, the integrated carrying capacity (ICC), which is a comprehensive capacity measure, is calculated in given space and time frames to support the evaluation of long-term sustainable regional economic development. Combined with the conceptual mode of “ driving force – pressure – state – response – control ” (D–PSR–C), the ICC reveals the loading capacity between human activity and the ecosystem. In coastal zones, the ICC is more effective because of the rapid socio-economic development. Using a case study in the Nantong coastal area in China, we constructed an index system to assess the regional ICC and its variability in 2005, 2008 and 2009. The results indicate that (1) almost all county ICC states of land, shoals, marine and regional are under-loading with several exceptions that are full-loading. The highest value is the regional ICC of Qidong in 2009 (2.679 and under-loading). The lowest value is the Haian marine ICC in 2008 (0.171 and over-loading). (2) From 2005 to 2009, the ICC values of beach, marine and regional were increasing, but it was decreasing in land. (3) Based on the analysis of the carrying capacity of the secondary indicator contribution rate, the population pressure and the external contribution ability are nearly below 10%, and environmental pollution is also a critical indicator. In this paper, the assessment indices system contains almost all of the impacts of human activity, and the calculation method and the judgment standard can improve the traditional method's faultiness. The conclusions are a fundamental basis for the long-term balance between economic development and ecosystem health.

While the development of social economy in the coastal areas, the demand of electricity is rising up and the construction of coastal power station is accelerating, but the discharge water is increasingly serious damage to the coastal sea. This paper introduces the system framework and the main function, which is based on GIS to monitor and evaluate the discharge water of power station, and divide the damage rank by using RS, monitoring analysis and discharge standard, so that we can calculate the compensation total value by using ecological compensating technology and draft the management program of the power station. The system can achieve the auto management of the power station, which can assure the normal working of power station and the health of eco-environment to balance the development of the economy and the eco-system.

To reduce typhoon-caused damages, numerical and empirical methods are often used to forecast typhoon storm surge. However, typhoon surge is a complex nonlinear process that is difficult to forecast accurately. We applied a principal component back-propagation neural network (PCBPNN) to predict the deviation in typhoon storm surge, in which data of the typhoon, upstream flood, and historical case studies were involved. With principal component analysis, 15 input factors were reduced to five principal components, and the application of the model was improved. Observation data from Huangpu Park in Shanghai, China were used to test the feasibility of the model. The results indicate that the model is capable of predicting a 12-hour warning before a typhoon surge.

The thermal plume discharged from the power plants has exerted many negative effects on the coastal environment by decreasing the dissolved oxygen, density and viscosity of water, speeding up the settlement of suspended mater, and changing the habit of life, growth and reproduction of marine organisms. To monitor the thermal pollution of cooling-water discharge from the coal-fired power plants and nuclear power plant in the eastern coastal region in China, the mono-window algorithm for retrieving sea surface temperature (SST) from thermal infrared bands of Landsat TM/ETM+, and HJ IRS data is improved by integrating the atmospheric correction model. The SST variability influenced by the thermal discharge from the coal-fired power plants and nuclear power plant near Xiangshan Harbour, Tianwan and Yueqing Bay in the eastern coastal area in China is investigated by comparing the distributions of SST around the power plants before and after the operation of the power plants. The results indicate that the spatial variability of SST rise depends on the installed capacity of power plants, coastal terrain, tide, etc. Although the area where the SST rise is more than 3°C was not large, thermal discharge still exerts impact on ecosystems within the bay to some degree due to the relatively closeness of the environment of the bay. Taking the coastal terrain near the power plants into consideration, more reasonable operation projects of the power plants and the design of the intake and discharge mouth should be set up in order to accelerate the dilution, diffusion and cooling of the thermal plume under the influence of discharged flow of the rivers, and reduce the impact of thermal pollution on the ecological community with high biodiversity in the intertidal zone.

Thermal power technology has brought great convenience for human electricity energy demand, but thermal discharge from thermal power plants has caused great harm to the coastal environment. Therefore, it’s important to strengthen the monitoring of thermal pollution from power plants for guarantee the normal operation of coastal environment and ecological system. Thermal infrared remote sensing technology provides a new measure for monitoring the thermal discharge. In this paper, we use mono-window algorithm and Landsat thermal infrared data to retrieved the sea surface temperature around Xiangshan Power Plants, and achieved the result of 1~5.4°C temperature rise in 106.52km2 in Xiangshan Harbor, revealed the spatial distribution regularities of thermal discharge and discussed the influence of thermal discharge on costal environment.

Thermal discharge pollution from the nuclear power plants has considerable impact on the coastal environment. The means of detecting thermal discharge from nuclear power plants by using satellites thermal infrared data can not only reduce costs and improve efficiency of monitoring, but also reflect the extent of spatial diffusion and spatial-temporal variability of thermal discharge on a large scale. In this research, multi-temporal Landsat TM and ETM+ band 6 thermal infrared images were used to quantify the variability of sea surface temperature (SST) around the Tianwan nuclear power plant in Tian Bay, Jiangsu Province, eastern China. The general patterns of SST variability during 2003–2009 indicated the extent of influence of thermal discharge from Tianwan nuclear power plant, and furthermore, the intensity and range of this influence were also investigated and compared in different seasons. The research results provided a basis for protection of the health of coastal marine ecosystems in Tian Bay.

Urban thermal environment is a complicated nonlinear system, which is affected by the integration of various influencing factors inside the city. To explore the mechanism of urban heat island (UHI) effect formation in the city of Shanghai, China, decision tree is applied to establishing the quantitative relationships between urban thermal environment and its influencing factors, which reflect ecological condition, built-up density and intensity, and human heat. The results indicate that it is feasible to simulate the intensity and distribution of land surface temperature (LST) field and predict the tendency of UHI effect in the future by decision tree. This research will help to take effective measures to alleviate heat island effect, and consequently reduce environmental pollution and improve urban entironment.

The forecasting of disaster weather using spatial data mining technique is still at an initial stage presently. Recent researches have indicated that intensive precipitation in the Yangtze River Basin in China is closely related to the activity of Mesoscale Convective Systems (MCS) moving out of the Tibetan Plateau in China, however, the factors that influence MCS trajectories are very complex. To discover the trajectories of MCS and features of environmental physical field favoring MCS origination and development over the Plateau, in this paper, MCS are automatically tracked over the Plateau using GMS infrared black-body temperature data. Based on these, spatial clustering method, CLARANS method, is applied to analyzing the characteristics of dynamical field, which influence MCS move eastward out of the Plateau in summer, using environmental physical field forecasting values. The results reveal that the methods are effective and valuable approaches to studying the conditions of environmental physical field favoring the trajectory and propagation of MCS, and improving the predictability of intensive convective weather.

The forecasting of disaster weather, especially intensive convective weather, is becoming increasingly important. Recent evidence has indicated that the intensive precipitation in the Changjiang River Basin and the south-west region of China is closely related to the activity of Mesoscale Convective Systems (MCS), which move out of the Tibetan Plateau in China. To date, however, the mechanism of MCS and their structure are not yet clear, due to restrictions on time and space scales in traditional observation data, especially in the Tibetan Plateau. To reveal MCS features and trajectories, in this paper, MCS are automatically tracked over the Tibetan Plateau using Geostationary Meteorological Satellite (GMS) infrared black-body temperature (Tbb) data from June to August 1998. Based on these, spatial clustering analysis is applied to study the relationships between the trajectories of MCS over the Tibetan Plateau and their environmental physical field values. And, the spatial distribution features of environmental physical field of influencing MCS move out of the Plateau are obtained. Consequently, these prove new methods to discover the regulations of MCS trajectories and the causes of MCS origination and development. Meanwhile, the results show that it is of great value in improving the precision of heavy rainfall and intensive convective weather forecasting in the Changjiang River Basin and the south-west region of China.