In the past decades, continuous efforts have been made at a national level to reduce Nitrogen Dioxide (NO₂) emissions in the atmosphere over China. However, public concern and related research mostly deal with tropospheric NO₂ columns rather than ground-level NO₂ concentrations, but actually ground-level NO₂ concentrations are more closely related to anthropogenic emissions, and directly affect human health. This paper presents one method to derive the ground-level NO₂ concentrations using the total column of NO₂ observed from the Ozone Monitoring Instrument (OMI) and the simulations from the Community Multi-scale Air Quality (CMAQ) model in China. One year’s worth of data from 2014 was processed and the results compared with ground-based NO₂ measurements from a network of China’s National Environmental Monitoring Centre (CNEMC). The standard deviation between ground-level NO₂ concentrations over China, the CMAQ simulated measurements and in-situ measurements by CNEMC for January was 21.79 μg/m³, which was improved to a standard deviation of 18.90 μg/m³ between our method and CNEMC data. Correlation coefficients between the CMAQ simulation and in-situ measurements were 0.75 for January and July, and they were improved to 0.80 and 0.78, respectively. Our results revealed that the method presented in this paper can be used to better measure ground-level NO₂ concentrations over China.

Onboard the Chinese GaoFen-5 (GF5) satellite, the Environmental trace gases Monitoring Instrument (EMI) is a nadir-viewing wide-field spectrometer that was launched on May 9, 2018. EMI measures the back-scattered earthshine solar radiance in the ultraviolet and visible spectral range. By using the differential optical absorption spectrometry (DOAS) method and the EMI measurements in the VIS1 band (405–465 nm), we performed retrievals of NO₂. Some first retrieval results of NO₂ from EMI and a comparison with OMI and TROPOMI products are presented in this paper. The monthly mean total vertical column densities (VCD) of NO₂ show similar spatial distributions to OMI and TROPOMI (r > 0.88) and their difference is less than 27%. A comparison of the daily total VCD shows that EMI could detect the NO₂ patterns in good agreement with OMI (r = 0.93) and TROPOMI (r = 0.95). However, the slant column density (SCD) uncertainty (0.79 × 10¹⁵ molec cm⁻²) of the current EMI algorithm is relatively larger than OMI. The daily variation pattern of NO₂ from EMI in Beijing in January 2019 is consistent with TROPOMI (r = 0.96). The spatial distribution correlation of the tropospheric NO₂ VCD of EMI with OMI and TROPOMI is 0.88 and 0.89, respectively, but shows an overestimate compared to OMI (15%) and TROPOMI (23%), respectively. This study demonstrates the capability of using EMI for global NO₂ monitoring.

Estimation of the number and geo-location of oil wells is important for policy holders considering their impact on energy resource planning. With the recent development in optical remote sensing, it is possible to identify oil wells from satellite images. Moreover, the recent advancement in deep learning frameworks for object detection in remote sensing makes it possible to automatically detect oil wells from remote sensing images. In this paper, we collected a dataset named Northeast Petroleum University–Oil Well Object Detection Version 1.0 (NEPU–OWOD V1.0) based on high-resolution remote sensing images from Google Earth Imagery. Our database includes 1192 oil wells in 432 images from Daqing City, which has the largest oilfield in China. In this study, we compared nine different state-of-the-art deep learning models based on algorithms for object detection from optical remote sensing images. Experimental results show that the state-of-the-art deep learning models achieve high precision on our collected dataset, which demonstrate the great potential for oil well detection in remote sensing.

Accurately estimating fine ambient particulate matter (PM_2.5) is important to assess air quality and to support epidemiological studies. To analyze the spatiotemporal variation of PM_2.5 concentrations, previous studies used different methodologies, such as statistical models or neural networks, to estimate PM_2.5. However, there is little research on full-coverage PM_2.5 estimation using a combination of ground-measured, satellite-estimated, and atmospheric chemical model data. In this study, the linear mixed effect (LME) model, which used the aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), meteorological data, normalized difference vegetation index (NDVI), and elevation data as predictors, was fitted for 2017 over Beijing–Tianjin–Hebei (BTH). The LME model was used to calibrate the PM_2.5 concentration using the nested air-quality prediction modeling system (NAQPMS) simulated with ground measurements. The inverse variance weighting (IVW) method was used to fuse satellite-estimated and model-calibrated PM_2.5. The results showed a strong agreement with ground measurements, with an overall coefficient (R²) of 0.78 and a root-mean-square error (RMSE) of 26.44 μg/m³ in cross-validation (CV). The seasonal R² values were 0.75, 0.62, 0.80, and 0.78 in the spring, summer, autumn, and winter, respectively. The fusion results supplement the lack of satellite estimates and can capture more detailed information than the NAQPMS model. Therefore, the results will be helpful for pollution process analyses and health-related studies.

The Himawari-8 geostationary weather satellite, which is an Earth observing satellite launched in October 2014, has been applied in climate, environment, and air quality studies. Using hourly observation data from the Advanced Himawari Imager (AHI) on board Himawari-8, a new dark target algorithm was proposed to retrieve the aerosol optical depth (AOD) at 1 km and 5 km resolutions over mainland China. Because of the short satellite operation time and lack of AErosol RObotic NETwork (AERONET) sites across China, we cannot derive robust and representative surface reflectance relationships for the visible to near-infrared channels by atmospheric correction. Therefore, we inherited the empirical reflectance relationship from the Moderate Resolution Imaging Spectroradiometer (MODIS) and we used the AHI and MODIS spectral response functions to make the relationship more suitable for AHI. Ultimately, our AOD products can better reflect the regional characteristics with the AHI sensor. Seasonal averages showed that our product is more similar to MODIS Collection 6 (C6) Dark Target (DT) AOD than the Japan Aerospace Exploration Agency (JAXA) AHI AOD, but the difference is largest in winter. In addition, we evaluated several satellite retrieval products (our AHI AOD, JAXA AHI AOD, the National Oceanic and Atmospheric Administration (NOAA) VIIRS AOD, MODIS DT AOD, and MODIS DB AOD) against AERONET AOD from July 2016 to June 2017. The results showed that our AHI measurements demonstrate good agreement with, but exhibit a little overestimation, as compared to ground-based AERONET measurements with a correlation coefficient of 0.83 and an root-mean-square error (RMSE) of 0.112. The hourly validation also showed stable statistical results. A time series comparison with ground-based observations from two AERONET sites (Beijing-CAMS and XiangHe) showed that our AHI AOD products have trends as those in MODIS DB AOD, but that the bias in Beijing-CAMS is positive and higher than that in XiangHe. This error and the slight overestimation may be caused by the single continental aerosol model assumption and not considering the Normalized Difference Vegetation Index (NDVI).

The rapid changes of aerosol sources in eastern China during recent decades could bring considerable uncertainties for satellite retrieval algorithms that assume little spatiotemporal variation in aerosol single scattering properties (such as single scattering albedo (SSA) and the size distribution for fine-mode and coarse mode aerosols) in East Asia. Here, using ground-based observations in six AERONET sites, we characterize typical aerosol optical properties (including their spatiotemporal variation) in eastern China, and evaluate their impacts on Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol retrieval bias. Both the SSA and fine-mode particle sizes increase from northern to southern China in winter, reflecting the effect of relative humidity on particle size. The SSA is ~0.95 in summer regardless of the AEROENT stations in eastern China, but decreases to 0.85 in polluted winter in northern China. The dominance of larger and highly scattering fine-mode particles in summer also leads to the weakest phase function in the backscattering direction. By focusing on the analysis of high aerosol optical depth (AOD) (>0.4) conditions, we find that the overestimation of the AOD in Dark Target (DT) retrieval is prevalent throughout the whole year, with the bias decreasing from northern China, characterized by a mixture of fine and coarse (dust) particles, to southern China, which is dominated by fine particles. In contrast, Deep Blue (DB) retrieval tends to overestimate the AOD only in fall and winter, and underestimates it in spring and summer. While the retrievals from both the DT and DB algorithms show a reasonable estimation of the fine-mode fraction of AOD, the retrieval bias cannot be attributed to the bias in the prescribed SSA alone, and is more due to the bias in the prescribed scattering phase function (or aerosol size distribution) in both algorithms. In addition, a large yearly change in aerosol single scattering properties leads to correspondingly obvious variations in the time series of MODIS AOD bias. Our results reveal that the aerosol single scattering properties in the MODIS algorithm are insufficient to describe a large variation of aerosol properties in eastern China (especially change of particle size), and can be further improved by using newer AERONET data.