Land use changes usually lead to the deterioration of freshwater ecosystems and reduced biodiversity. Aquatic organisms are considered valuable indicators for reflecting the conditions of freshwater ecosystems. Understanding the relationship between organisms and land use type, as well as physiochemical conditions, is beneficial for the management, monitoring and restoration of aquatic ecosystems. In this study, fish, macroinvertebrates, and diatoms were investigated at 60 sampling sites in the Wei River basin from October 2012 to April 2013 to determine the relationships between the environment and aquatic organisms. The richness, abundance, Shannon diversity, evenness, Margalef diversity, and Simpson diversity were selected as biological indices for analyzing the correlation between these communities and environmental variables according to Pearson’s coefficient. Canonical correspondence analysis (CCA) was used to analyze the relationship between the biotic communities and environmental variables. The results showed that three diatom indices were weakly correlated with chemical oxygen demand (COD), qualitative Habitat Evaluation Index (QH), and dissolved oxygen (DO). Four macroinvertebrate indices were associated with total phosphorus (TP) while total nitrogen (TN), and agricultural land (AL) had a significant influence on assemblages, suggesting that macroinvertebrates could respond to nutrient levels in the Wei River basin. All land use types had a strong effect on fish indices except AL, indicating that fish would be better used as indicators of spatial changes in the aquatic ecosystem. In conclusion, fish and macroinvertebrates have the potential for use in routine monitoring programs in the Wei River basin.

Several epidemiological studies have indicated that an increased risk of depression was associated with long-term exposure to PM2.5. The objective of our two-sample Mendelian randomization study was to determine the causal relationship between long-term exposure to particulate matter 2.5 and the risk of depression. A two-sample Mendelian randomization study was performed based on GWAS summary data. Forty-six PM2.5-related single nucleotide polymorphisms were suitable for the analysis as instrumental variables. The random-effect model of inverse-variance weighted and the other four methods (weighted median, MR-Egger, Simple mode and weighted mode) were all performed for the analysis. Additionally, multivariate Mendelian randomization analysis was also completed. Our two-sample Mendelian randomization study indicated that exposure to particulate matter 2.5 has a significantly positive impact on the risk of depression (P = 0.026, random-effect model of inverse-variance weighted). After adjusting for smoking and body mass index in our multivariate Mendelian randomization analysis separately, the relationship between exposure to PM2.5 and the risk of depression remained significant. Based on current GWAS data, our study supplies potential evidence that long-term exposure to PM2.5 is a risk factor for depression. The improvement in air quality may be conducive to reducing the risk of depression.