The relationship between air quality and MODIS aerosol optical depth in major cities of the Yangtze River Delta

A novel framework to predict chlorophyll-a concentrations in water bodies through multi-source big data and machine learning algorithms

Eutrophication happens when water bodies are enriched by minerals and nutrients. Dense blooms of noxious are the most obvious effect of eutrophication that harms water quality, and by increasing toxic substances damage the water ecosystem. Therefore, it is critical to monitor and investigate the development process of eutrophication. The concentration of chlorophyll-a (chl-a) in water bodies is an essential indicator of eutrophication in them. Previous studies in predicting chlorophyll-a concentrations suffered from low spatial resolution and discrepancies between predicted and observed values. In this paper, we used various remote sensing and ground observation data and proposed a novel machine learning-based framework, a random forest inversion model, to provide the spatial distribution of chl-a in 2 m spatial resolution. The results showed our model outperformed other base models, and the goodness of fit improved by over 36.6% while MSE and MAE decreased by over 15.17% and over 21.26% respectively. Moreover, we compared the feasibility of GF-1 and Sentinel-2 remote sensing data in chl-a concentration prediction. We found that better prediction results can be obtained by using GF-1 data, with the goodness of fit reaching 93.1% and MSE only 3.589. The proposed method and findings of this study can be used in future water management studies and as an aid for decision-makers in this field.

Woody and herbaceous wastes for the remediation of polluted waters of wetlands

Plants wastes play an important role during water remediation in wetlands. Plant waste is made into biochar, which is usually used directly or as a water biofilter to remove pollutants. While, the water remediation effect of combination for biochar from woody and herbaceous wastes coupling with substrate types in CWs have not been fully explored. To explore the water remediation effect of combination for biochar coupling with substrate on pH, Turbidity, COD, NH₄⁺-N, TN and TP, four plant configuration modes combining seven woody plants and eight herbaceous plants (Plants A, Plants B, Plants C, Plants D) were coupled with three substrate types (Substrate 1, Substrate 2, Substrate 3) as 12 experiment groups, using water detection methods and significant differences test (LSD) to analyze. Results showed: (1) Compared to Substrate 3, Substrate 1 and Substrate 2 removed significantly higher in pollutants concentration (p < 0.05); (2) NH₄⁺-N final concentration in Plants C and Plants D were both significantly lower than Plants A and Plants B coupling with Substrate 1 and Substrate 2 (p < 0.05). The TN final concentration of Plants C was significantly lower than Plants A in Substrate 1 (p < 0.05), and Plants A's turbidity was significantly lower than Plants C and Plants D's in Substrate 2 (p < 0.05); (3) The pollutants removal of group A1, A2, B1, B2, C1, C2, D1 and D2 were significantly higher than other experiment groups (p < 0.05). Group A2, B2, C1 and D1 had the best water remediation effect and better stability of plant community. Findings in this study will be beneficial for remediating polluted water and building sustainable wetlands.

Spatiotemporal transmission of infectious nanochemical particles in water environment: A case study of Covid-19

This study explores the dynamic transmission of nanochemical infectious particles due to COVID-19 in the water environment using a spatiotemporal epidemiological approach. We proposed a novel multi-agent model to simulate the spread of COVID-19 by considering several influencing factors. The model divides the population into susceptible and infected and analyzes the impact of different prevention and control measures, such as limiting the number of people and wearing masks on the spread of COVID-19. The findings suggest that reducing population density and wearing masks can significantly reduce the likelihood of virus transmission. Specifically, the research shows that if the population moves within a fixed range, almost everyone will eventually be infected within 1 h. When the population density is 50%, the infection rate is as high as 96%. If everyone does not wear a mask, nearly 72.33% of the people will be infected after 1 h. However, when people wear masks, the infection rate is consistently lower than when they do not wear masks. Even if only 25% of people wear masks, the infection rate with masks is 27.67% lower than without masks, which is strong evidence of the importance of wearing a mask. As people's daily activities are mostly carried out indoors, and many super-spreading events of the new crown epidemic also originated from indoor gatherings, the research on indoor epidemic prevention and control is essential. This study provides decision-making support for epidemic preventions and controls and the proposed methodology can be used in other regions and future epidemics.

Evaluation of different machine learning approaches and aerosol optical depth in PM2.5 prediction

Atmospheric Aerosol Optical Depth (AOD), derived from polar-orbiting satellites, has shown potential in PM_2.5 predictions. However, this important source of data suffers from low temporal resolution. Recently, geostationary satellites provide AOD data in high temporal and spatial resolution. However, the feasibility of these data in PM_2.5 prediction needs further study. In this paper, we analyzed the impact of AOD derived from Himawari-8 in PM_2.5 predictions. Moreover, by combining wavelet, machine learning techniques, and minimum redundancy maximum relevance (mRMR), a novel hybrid model was proposed. The results showed that AOD missing rate over Yangtze River Delta region is the highest in Nanjing, Hefei, and Maanshan. In addition, missing rates are the lowest in winter and summer (∼80%). Moreover, we found that considering AOD, as an auxiliary variable in the model, could not improve the accuracy of PM_2.5 predictions, and in some cases decreased it slightly. In comparison with other models, our proposed hybrid model showed higher prediction accuracy, R² is improved by 11.64% on average, and root mean square error, mean absolute error, and mean absolute percentage error is reduced by 26.82%, 27.24%, and 29.88% respectively. This research provides a general overview of the availability of Himawari-8 AOD data and its feasibility in PM_2.5 predictions. In addition, it evaluates different machine learning approaches in PM_2.5 predictions. Our proposed framework can be used in other regions to predict different air pollutants concentrations and can be used as an aid for air pollution controlling programs.