Fugitive dust deriving from construction sites is a serious local source of particulate matter (PM) that leads to air pollution in cities undergoing rapid urbanization in China. In spite of this fact, no study has yet been published relating to prediction of high levels of PM with diameters <10 μm (PM₁₀) as adjudicated by the Individual Air Quality Index (IAQI) on fugitive dust from nearby construction sites. To combat this problem, the Construction Influence Index (Ci) is introduced in this article to improve forecasting models based on three neural network models (multilayer perceptron, Elman, and support vector machine) in predicting daily PM₁₀ IAQI one day in advance. To obtain acceptable forecasting accuracy, measured time series data were decomposed into wavelet representations and wavelet coefficients were predicted. Effectiveness of these forecasters were tested using a time series recorded between January 1, 2005, and December 31, 2011, at six monitoring stations situated within the urban area of the city of Wuhan, China. Experimental trials showed that the improved models provided low root mean square error values and mean absolute error values in comparison to the original models. In addition, these improved models resulted in higher values of coefficients of determination and AHPC (the accuracy rate of high PM₁₀ IAQI caused by nearby construction activity) compared to the original models when predicting high PM₁₀ IAQI levels attributable to fugitive dust from nearby construction sites.

The importance of atmospheric correction is pronounced for retrieving physical parameters in aquatic systems. To improve the retrieval accuracy of trophic level index (TLI), we built eight models with 43 samples in Wuhan and proposed an improved method by taking atmospheric water vapor (AWV) information and Landsat-8 (L8) remote sensing image into the input layer of radical basis function (RBF) neural network. All image information taken in RBF have been radiometrically calibrated. Except model(a), image data used in the other seven models were not atmospherically corrected. The eight models have different inputs and the same output (TLI). The models are as follows: (1) model(a), the inputs are seven single bands; (2) model(c), besides seven single bands (b1, b2, b3, b4, b5, b6, b7), we added the AWV parameter k1 to the inputs; (3) model(c1), the inputs are AWV difference coefficient k2 and the seven bands; (4) model(c2), the input layers include seven single bands, k1 and k2; (5) model(b), seven band ratios (b3/b5, b1/b2, b3/b7, b2/b5, b2/b7, b3/b6, and b3/b4) were used as input parameters; (6) model(b1), the inputs are k1 and seven band ratios; (7) model(b2), the inputs are k2 and seven band ratios; (8) model(b3), the inputs are k1, k2, and seven band ratios. We estimated models with root mean squared error (RMSE), model(a) > model(b3) > model(b1) > model(c2) > model(c) > model(b) > model(c1) > model(b2). RMSE of the eight models are 12.762, 11.274, 10.577, 8.904, 8.361, 6.396, 5.389, and 5.104, respectively. Model b2 and c1 are two best models in these experiments, which confirms both the seven single bands and band ratios with k2 are superior to other models. Results also corroborate that most lakes in Wuhan urban area are in mesotrophic and light eutrophic states.

Secchi disk depth (Z_SD) is used to quantify water clarity as an important water-quality parameter, and one of the most used mechanistic models for Z_SD is the quasi-analytical algorithm (QAA), of which the latest version is QAA_v6. There are two models in QAA for clear and turbid waters (referred to as QAA_clear and QAA_turbid). QAA_v6 switches between the two models by setting a threshold value for the remote sensing reflectance (R_rs, sr⁻¹) at the selected reference band of 656 nm. However, some researchers found that this reference band or the threshold value does not apply to many turbid inland lakes. In Honghu Lake, the R_rs (656) (R_rs at 656 nm) in the whole lake is less than 0.0015 sr⁻¹; therefore, only QAA_turbid can be applied. Moreover, we found that QAA_clear resulted in overestimation while QAA_turbid resulted in significant underestimations. The waters of inland lakes usually continuously vary between clear and turbid water. We proposed a hypothesis that QAA_turbid and QAA_clear transition evenly, rather than being distinguished by one threshold value, and we developed a model that combined QAA_clear and QAA_turbid according to our assumption. This model simulated the process of continuous change in water clarity. The results showed that our model had a better performance with an RMSE that reduced from 0.5 to 0.28, an MAE that reduced from 0.43 to 0.21, and bias that reduced from −0.4 to −0.05 m compared with QAA_v6. We applied QAA_Honghu to Landsat TM, ETM+, and OLI data and obtained 205 Z_SD maps with high spatial resolution in Honghu Lake. The results were consistent with the existing in situ measurements. From 1987–2020, the Z_SD results of Honghu Lake showed an overall downward trend and a distinct seasonal pattern.

The Yangtze River ecosystem is one of the most important ecosystems in central China. Due to the substantial increase in China’s economy and the improvement of living conditions in rural areas over the past decade, domestic wastewater discharges in rural areas have increased dramatically, seriously affecting the Yangtze River Basin environment, and becoming one of the main obstacles to achieving global sustainable development goals (SDGs). Rural environmental management is very important for the prevention and control of water pollution in the Yangtze River Basin. In order to address the problem of rural domestic sewage treatment, this paper proposes a domestic sewage collection model applicable to rural areas. An operable village sewage treatment plan was developed for Jiangxia District, Wuhan City, specific for each village, based on a previous village sewage treatment plan in Jiangxia District. The plan is cost-effective and ensures a suitable quality of the treated domestic wastewater that meets the local discharge and reuse standards, thus reducing pollution of the Yangtze River and contributing to the restoration of the Yangtze ecological environment.

Total phosphorus (TP, a non-optical sensitivity parameter) has become the primary pollutant in the Yangtze River, the third largest river in the world. It is strongly correlated with turbidity (an optical sensitivity parameter) in rivers. In this study, we constructed a turbidity-mediated TP retrieval model using Sentinel-2 observations and field-measured daily-scale water quality. The model was successfully applied to estimate the temporal and spatial variations of TP concentration in the middle reaches of the Yangtze River (MYR) from 2020 to 2023. Our results show: (1) the model accuracy of TP concentration retrieval with turbidity is significantly higher (R² = 0.71, MAPE = 15.78%) than that for the model without turbidity (R² = 0.62, MAPE = 16.38%); (2) the turbidity and TP concentration in the MYR is higher in summer and autumn than in winter and spring; and (3) the turbidity and total phosphorus (TP) concentration of the Yangtze River showed a significant increase after passing through Dongting Lake (p < 0.05).