A long-term dataset, including physicochemical, nutrient, and phytoplankton assemblages from 1994 to 2016, was analyzed to investigate the response of the algal community to variations in environmental factors in Deep Bay and Mirs Bay in southern China. These bays differ in their overall nutrient loadings, as well as in physical factors. The results showed that diatoms were numerically dominant in Mirs Bay, while other minor phytoplankton groups, including eutrophication-tolerant species, constituted the majority in Deep Bay. Phytoplankton community composition tended to be less complex in Deep Bay, suggesting a stressed, unstable and unbalanced ecosystem compared to that in Mirs Bay. Algal blooms occurred more frequently in Mirs Bay, whereas fewer but larger-scale blooms occurred in Deep Bay. Statistically, the combination of all explanatory variables accounted for approximately 55% of the variation in Chlorophyll-a (Chl-a) concentration and less than 20% of the total phytoplankton variation over the 23-year period in the two bays. The high level of nutrients caused by urbanization was not the driving force in the formation of blooms but presumably provided a nutrient base that resulted in blooms with longer durations and covering larger areas.Regarding the continuous worsening of tropospheric ozone pollution, the scenario in Shanghai is a microcosm of the entire China. Understanding the ozone formation regimes (OFRs), their variations, and driving factors is a prerequisite for formulating effective ozone control strategies. Traditional OFR estimation by numerical model, which often involves sensitivity analysis on at least tens of scenarios, is labor-intensive and time-consuming; therefore, it is not appropriate to make OFR forecasts to guide ozone contingency control. In this study, by using a localized modeling system consisting of the Weather Research and Forecasting, Sparse Matrix Operator Kernel Emissions, and Community Multiscale Air Quality models and considering the latest emission inventory over the Yangtze River Delta of China, we discovered a strong connection between the variations of large-scale circulation (LSC) and OFRs over Shanghai in July 2017, thereby providing an alternative way to infer OFR. During the northward movement of Western Pacific Subtropical High from South China Sea, the wind field over Shanghai changed from weak westerly to moderate southwesterly and to one without a distinct direction. The local OFR shifted from anthropogenic volatile organic compounds (AVOCs)-limited to NOx-limited and ultimately to the transitional regime. Such a variation in OFR is essentially driven by the spatial heterogeneity of NOx and AVOC emissions in different directions of Shanghai, brought on by the wind under different LSC patterns. With the existing weather forecasting technology, the LSC patterns can be well-predicted 48-72 h in advance. Hence, we propose the adoption of a dynamic ozone control strategy for Shanghai with the priority control target on AVOC or NOx emission sources adjusted according to the LSC pattern and OFR forecasts in a forthcoming O3 pollution episode. This would serve to maximize the peak ozone reduction under varying pollution conditions.The extent of prescription and illicit drug abuse in geographically isolated rural and micropolitan communities in the intermountain western United States (US) has not been well tracked. The goal of this pilot study was to accurately measure drug dose consumption rates (DCR) between two select populations, normalize the data and compare the DCRs to similar communities. To learn about patterns of drug abuse between the two disparate communities, we used the emergent field of wastewater-based epidemiology (WBE). A rapid, quantitative and systematic process for the determination of multiple classes of prescribed and illicit drugs was applied to influent wastewater samples. Influent samples were collected over the course of three months (April to June 2019) at two wastewater treatment plants representing a small urban and a rural community. Collection of sewage influent included 24-h composite samples and the use of polar organic chemical integrative samplers (POCIS), time-weighted samplers. Using the results from the composite sampling data, DCRs per 1000 population could be calculated from the concentration data and the use of excretion correction factors. The following 18 compounds amphetamine, methamphetamine, MDA, MDMA, morphine, 6-acetylmorphine, methadone, EDDP, codeine, benzoylecgonine, hydrocodone, hydromorphone, oxycodone, noroxycodone, ketamine, fluoxetine, tramadol, and ritalinic acid; represent a subset of the targeted analytes that were consistently measured at detectable concentration levels, and present at both sites. Following normalization of the drug measurements to influent flow rates and per capita, the small urban community demonstrated greater collective excretion rates (CER) than the rural community, with the exceptions of amphetamine and methamphetamine.The elimination of organic micropollutants (OMPs) from wastewater could in future become mandatory for operators of wastewater treatment plants (WWTPs). Indicator substances are a great help and a cost-efficient way in monitoring the pollution of water bodies with OMPs caused by the discharge of WWTPs. However, with the still increasing number of OMPs in our environment, the selection of suitable indicator substances presents a challenge. A concept was developed to help identify representative indicator substances. The derived indicator substances are not only used to assess water pollution, but can also be used to calculate elimination efficiencies of WWTPs. In the present investigations, the indicator substances were used to evaluate the reduction of OMPs in the water body on the basis of the expansion of a WWTP with an ozonation plant. check details The transferability of the tool was verified with a second WWTP. Furthermore, the impact of the number of measurements was analysed via statistical combinatorics. With the tool, 36 substances were classified, leading to the identification of 9 suggested indicator substances. Among them ibuprofen and diclofenac attracted attention due to their ecotoxicological relevance. Detailed data analyses were carried out using principal component analysis (PCA) and loads.