Spatial-temporal characteristics and driving factors' contribution and evolution of agricultural non-CO2 greenhouse gas emissions in China: 1995-2021.

Comprehending the spatial-temporal characteristics, contributions, and evolution of driving factors in agricultural non-CO2 greenhouse gas (GHG) emissions at a macro level is pivotal in pursuing temperature control objectives and achieving China's strategic goals related to carbon peak and carbon neutrality. This study employs the Intergovernmental Panel on Climate Change (IPCC) carbon emissions coefficient method to comprehensively evaluate agricultural non-CO2 GHG emissions at the provincial level. Subsequently, the contributions and spatial-temporal evolution of six driving factors derived from the Kaya identity were quantitatively explored using the Logarithmic Mean Divisia Index (LMDI) and Geographical and Temporal Weighted Regression (GTWR) methods. The results revealed that the distribution of agricultural non-CO2 GHG emissions is shifting from the central provinces to the northwest regions. Moreover, the dominant driving factors of agricultural non-CO2 GHG emissions were primarily economic factor (EDL) with positive impact (cumulative promotion is 2939.61 million metric tons (Mt)), alongside agricultural production efficiency factor (EI) with negative impact (cumulative reduction is 2208.98 Mt). Influence of EDL diminished in the eastern coastal regions but significantly impacted underdeveloped regions such as the northwest and southwest. In the eastern coastal regions, EI gradually became the absolute dominant driver, demonstrating a rapid reduction effect. Additionally, a declining birth rate and rural-to-urban population migration have significantly amplified the driving effects of the population factor (RP) at a national scale. These findings, in conjunction with the disparities in geographic and socioeconomic development among provinces, can serve as a guiding framework for the development of a region-specific roadmap aimed at reducing agricultural non-CO2 GHG emissions.

Short-term air quality forecasting model based on hybrid RF-IACA-BPNN algorithm.

Despite the apparent improvement in air quality in recent years through a series of effective measures, the concentration of PM2.5 and O3 in Chengdu city remains high. And both the two pollutants can cause serious damage to human health and property; consequently, it is imperative to accurately forecast hourly concentration of PM2.5 and O3 in advance. In this study, an air quality forecasting method based on random forest (RF) method and improved ant colony algorithm coupled with back-propagation neural network (IACA-BPNN) are proposed. RF method was used to screen out highly correlated input variables, and the improved ant colony algorithm (IACA) was adopted to combine with BPNN to improve the convergence performance. Two datasets based on two different kinds of monitoring stations along with meteorological data were applied to verify the performance of this proposed model and compared with another five plain models. The results showed that the RF-IACA-BPNN model has the minimum statistical error of the mean absolute error, root mean square error, and mean absolute percentage error, and the values of R2 consistently outperform other models. Thus, it is concluded that the proposed model is suitable for air quality prediction. It was also detected that the performance of the models for the forecasting of the hourly concentrations of PM2.5 were more acceptable at suburban station than downtown station, while the case is just the opposite for O3, on account of the low variability dataset at suburban station.

Migration of leather tannins and chromium in soils under the effect of simulated rain.

Chromium (Cr) and tannin are two major pollutants in leather industry. However, little is known about the co-migration of leather tannins and Cr in soils. In this study, column experiments were conducted to estimate Cr leaching behavior from topsoil and the environmental risk of the leachate at various tannin dosages and different ways (tannin either directly adding to the Cr-contaminated soil or adding stepwise through simulated rain) into the soil. The total Cr concentration in leachate was positively related with tannin content in soil, while Cr (Ⅵ) concentration was negatively correlated. The maximum cumulative leaching efficiency of total Cr from soil after six leaching events was 44.65% with 3 mg/g tannin adding into soil directly, and the maximum cumulative leaching efficiency of Cr (Ⅵ) was 38.75% with simulated rain leaching Cr-contaminated soil. With 3 mg/g tannin adding into soil, tannin concentration in the top layer (0-7 cm) lost by 32.67% after leaching, the amount of decomposed tannin was 0.25 mg/g, excluding the amount of tannin in leachate (3.63 mg/L) and the original amount in the soil (0.34 mg/g), indicating a slow degradation under natural condition. Both of the total Cr and Cr (Ⅵ) concentration in each layer of the soil columns decreased under tannin treatments compared with control. Compared with tannin adding stepwise into simulated rain, adding tannin into soil significantly (p < 0.05) affected the migration of Cr. Tannin increased the residual fraction while decreased the exchangeable fraction of Cr in the soils. Overall, this research can provide reference information for environmental risk assessment of contaminants in tanning sites.