Spatiotemporal Dynamics of Surface Ozone and Its Relationship with Meteorological Factors over the Beijing-Tianjin-Tangshan Region, China, from 2016 to 2019.

In recent years, ozone pollution has been increasing in some parts of the world. In this study, we used the Beijing-Tianjin-Tangshan (BJ-TJ-TS) urban agglomeration region as a case study and used satellite remotely sensed inversion data and hourly ground monitoring observations of surface ozone concentrations, meteorological data, and other factors from 2016 to 2019 to explore the spatiotemporal dynamic characteristics of surface ozone concentration and its pollution levels. We also investigated their coupling relationships with meteorological factors, including temperature, pressure, relative humidity, wind velocity, and sunshine duration, in order to support the development of effective control measures for regional ozone pollution. The results revealed that the surface ozone concentration throughout the BJ-TJ-TS region from 2016 to 2019 exhibited an overall pattern of high values in the northwest and low values in the southeast, as well as an obvious difference between built-up and non-built-up areas (especially in Beijing). Meanwhile, a notable increasing trend of ozone levels was discovered in the BJ and TJ areas from 2016 to 2019, whereas this upward trend was not evident in the TS area. In all three areas, the highest monthly average ozone values occurred in the summer month of June, while the lowest monthly average levels occurred in the winter month of December. Their diurnal variation values reached a maximum value at approximately 3:00-4:00 p.m. and a minimum value at approximately 7:00 a.m. It is clear that high temperature, long sunshine duration, low atmospheric pressure, and weak wind velocity conditions, as well as certain relative humidity levels, readily led to high-concentration ozone pollution. Meanwhile, the daily average values of the five meteorological factors on days with Grade I and Grade II ozone pollution displayed different characteristics.

Characteristics of aerosol optical depth dynamics and their causes over typical cities along the 21st Century Maritime Silk Road.

Atmospheric aerosols, i.e., suspension of liquid and/or solid particles in air, have serious impacts on human health. Exploring the variation and patterns of regional atmospheric aerosols is of great significance to monitor and evaluate atmosphere quality, especially in urban areas with large population. Here, with nine typical pivotal cities along the 21st Century Maritime Silk Road through Southeast Asia, South Asia to West Asia as case studies, based on MCD19A2 550 nm AOD products, combined meteorological factors, land use data, and nighttime light data, we analyzed the spatio-temporal distribution, variation features, influencing and/or driving factors of aerosols in developed urban areas over Asia. The results showed that the descending sequence of the annual aerosol optical depth (AOD) of the nine cities was Karachi, Doha, Chittagong, Bangkok, Colombo, Ho Chi Minh, Singapore, Gwadar, and Yangon during 2013-2018. Due to the influence of regional climate system and atmospheric aerosol types, the time series of annual, seasonal, and monthly AODs were significantly different. The high values of AODs in most cities were mainly located in the urban center or rapid socio-economic (e.g., industrial and agricultural) development regions. The effects of different meteorological factors on the AODs varied in different cities. The rainfall, relative humidity, and wind speed had great impacts on AODs in Ho Chi Minh, Bangkok, Singapore, and Yangon. Temperature, relative humidity, and wind speed had close correlations with AODs in Chittagong, Colombo, Karachi, and Gwadar of South Asia and Doha in West Asia. The urban area's AOD was influenced by the combined and synergistic effects of socio-economy, urbanization, and meteorological factors, with that in Karachi being the most significant.

Chimney Detection Based on Faster R-CNN and Spatial Analysis Methods in High Resolution Remote Sensing Images.

Spatially location and working status of pollution sources are very important pieces of information for environment protection. Waste gas produced by fossil fuel consumption in the industry is mainly discharged to the atmosphere through a chimney. Therefore, detecting the distribution of chimneys and their working status is of great significance to urban environment monitoring and environmental governance. In this paper, we use an open access dataset BUAA-FFPP60 and the faster regions with convolutional neural network (Faster R-CNN) algorithm to train the preliminarily detection model. Then, the trained model is used to detect the chimneys in three high-resolution remote sensing images of Google Maps, which is located in Tangshan city. The results show that a large number of false positive targets are detected. For working chimney detection, the recall rate is 77.27%, but the precision is only 40.47%. Therefore, two spatial analysis methods, the digital terrain model (DTM) filtering, and main direction test are introduced to remove the false chimneys. The DTM is generated by ZiYuan-3 satellite images and then registered to the high-resolution image. We set an elevation threshold to filter the false positive targets. After DTM filtering, we use principle component analysis (PCA) to calculate the main direction of each target image slice, and then use the main direction to remove false positive targets further. The results show that by using the combination of DTM filtering and main direction test, more than 95% false chimneys can be removed and, therefore, the detection precision is significantly increased.

Spatial and Temporal Distribution of Multiple Cropping Indices in the North China Plain Using a Long Remote Sensing Data Time Series.

Multiple cropping provides China with a very important system of intensive cultivation, and can effectively enhance the efficiency of farmland use while improving regional food production and security. A multiple cropping index (MCI), which represents the intensity of multiple cropping and reflects the effects of climate change on agricultural production and cropping systems, often serves as a useful parameter. Therefore, monitoring the dynamic changes in the MCI of farmland over a large area using remote sensing data is essential. For this purpose, nearly 30 years of MCIs related to dry land in the North China Plain (NCP) were efficiently extracted from remotely sensed leaf area index (LAI) data from the Global LAnd Surface Satellite (GLASS). Next, the characteristics of the spatial-temporal change in MCI were analyzed. First, 2162 typical arable sample sites were selected based on a gridded spatial sampling strategy, and then the LAI information was extracted from the samples. Second, the Savizky-Golay filter was used to smooth the LAI time-series data of the samples, and then the MCIs of the samples were obtained using a second-order difference algorithm. Finally, the geo-statistical Kriging method was employed to map the spatial distribution of the MCIs and to obtain a time-series dataset of the MCIs of dry land over the NCP. The results showed that all of the MCIs in the NCP showed an increasing trend over the entire study period and increased most rapidly from 1982 to 2002. Spatially, MCIs decreased from south to north; also, high MCIs were mainly concentrated in the relatively flat areas. In addition, the partial spatial changes of MCIs had clear geographical characteristics, with the largest change in Henan Province.