As the most abundant greenhouse gas in the atmosphere, CO2 has a significant impact on climate change. Therefore, the determination of the temporal and spatial distribution of CO2 is of great significance in climate research. However, existing CO2 monitoring methods have great limitations, and it is difficult to obtain large-scale monitoring data with high spatial resolution, thus limiting the effective monitoring of carbon sources and sinks. To obtain complete Chinese daily-scale CO2 information, we used OCO-2 XCO2 data, Carbon Tracker XCO2 data, and multivariate geographic data to build a model training data set, which was then combined with various machine learning models including Random Forest, Extreme Random Forest, XGBoost, LightGBM, and CatBoost. The results indicated that the Random Forest model presented the best performance, with a cross-validation R2 of 0.878 and RMSE of 1.123 ppm. According to the final estimation results, in terms of spatial distribution, the highest multi-year average RF XCO2 value was in East China (406.94 ± 0.65 ppm), while the lowest was in Northwest China (405.56 ± 1.43 ppm). In terms of time, from 2016 to 2018, the annual XCO2 in China continued to increase, but the growth rate showed a downward trend. In terms of seasonal effects, the multi-year average XCO2 was highest in spring (407.76 ± 1.72 ppm) and lowest in summer (403.15 ± 3.36ppm). Compared with the Carbon-Tracker data, the XCO2 data set constructed in this study showed more detailed spatial changes, thus, can be effectively used to identify potentially important carbon sources and sinks.

NO2 (nitrogen dioxide) is a common pollutant in the atmosphere that can have serious adverse effects on the health of residents. However, the existing satellite and ground observation methods are not enough to effectively monitor the spatiotemporal heterogeneity of near-surface NO2 concentrations, which limits the development of pollutant remediation work and medical health research. Based on TROPOMI-NO2 tropospheric column concentration data, supplemented by meteorological data, atmospheric condition reanalysis data and other geographic parameters, combined with classic machine learning models and deep learning networks, we constructed an ensemble model that achieved a daily average near-surface NO2 of 0.03° exposure. In this article, a meteorological hysteretic effects term and a spatiotemporal term were designed, which considerably improved the performance of the model. Overall, our ensemble model performed better, with a 10-fold CV R2 of 0.89, an RMSE of 5.62 µg/m3, and an MAE of 4.04 µg/m3. The model also had good temporal and spatial generalization capability, with a temporal prediction R2 and a spatial prediction R2 of 0.71 and 0.81, respectively, which can be applied to a wider range of time and space. Finally, we used an ensemble model to estimate the spatiotemporal distribution of NO2 in a coastal region of southeastern China from May 2018 to December 2020. Compared with satellite observations, the model output results showed richer details of the spatiotemporal heterogeneity of NO2 concentrations. Due to the advantages of using multi-source data, this model framework has the potential to output products with a higher spatial resolution and can provide a reference for downscaling work on other pollutants.

2014): An examination of a partial least squares-based dynamic water quota model for urban industries: a case study of the Wuhan City hospital industry, Urban Water Journal,The establishment of water quotas has an important practical significance in promoting urban standards for water utilization. Currently, industrial water quotas are highly arbitrary, inadequately restrictive, and impractical. This paper considers the example of the Wuhan City hospital industry. Common factors of industrial water use and major differences in water utilization structures were considered, and the principles of partial least squares (PLS) analysis were applied to establish an evaluation model for water utilization levels in this industry. Residuals were used to introduce the corresponding adjustment coefficients, and a dynamic model of water quotas in the hospital industry was constructed. Experimental results revealed that for this dynamic model, 80% of the samples examined exhibited errors of j20%j or less; thus, the dynamic approach was superior to traditional approaches for quota determination, where only 40% of samples had errors of j20%j or less.

No abstract