The application of multivariate statistical techniques including cluster analysis and principal component analysis-multiple linear regression (PCA-MLR) was successfully used to classify the river pollution level in Taiwan and identify possible pollution sources. Water quality and heavy metal monitoring data from the Taiwan Environmental Protection Administration (EPA) was evaluated for 14 major rivers in four regions of Taiwan with the Erren River classified as the most polluted river in the country. Biochemical oxygen demand (6.1 ± 2.38), ammonia (3.48 ± 3.23), and total phosphate (0.65 ± 0.38) mg/L concentration in this river was the highest of the 14 rivers evaluated. In addition, heavy metal levels in the following rivers exceeded the Taiwan EPA standard limit (lead: 0.01, copper: 0.03, and manganese: 0.03) mg/L concentration: lead-in the Dongshan (0.02 ± 0.09), Jhuoshuei (0.03 ± 0.03), and Xinhuwei Rivers (0.02 ± 0.02) mg/L; copper: in the Dahan (0.036 ± 0.097), Laojie (0.06 ± 1.77), and Erren Rivers are (0.05 ± 0.158) mg/L; manganese: in all rivers. A total 72% of the water pollution in the Erren River was estimated to originate from industrial sources, 16% from domestic black water, and 12% from natural sources and runoff from other tributaries. Our research demonstrated that applying PCA-MLR and cluster analysis on long-term monitoring water quality would provide integrated information for river water pollution management and future policy making.

Treatment cost and quality of domestic water are highly correlated with raw water quality in reservoirs. This study aims to identify the key factors that influence the trophic state levels and correlations among Carlson trophic state index (CTSI) levels, water quality parameters and weather factors in four major reservoirs in Taiwan from 2000 to 2017. Weather (e.g., air temperature, relative humidity, total precipitation, sunlight percentage and cloud cover) and water quality parameters (e.g., pH, chemical oxygen demand, suspended solids (SS), ammonia, total hardness, nitrate, nitrite and water temperature) were included in the principal component analysis and absolute principal component score models to evaluate the main governing factors of the trophic state levels (e.g., CTSI). SS were washed out by precipitation, thereby influencing the reservoir transparency tremendously and contributing over 50% to the CTSI level in eutrophicated reservoirs (e.g., the Shihmen and Chengchinghu Reservoirs). CTSI levels in the mesotrophic reservoir (e.g., Liyutan Reservoir) had strong correlation with chlorophyll-a and total phosphorus. Results show that rainfall/weather factors were the key driving factors that affected the CTSI levels in Taiwan eutrophicated reservoirs, indicating the need to consider basin management and the impacts of extreme precipitation in reservoir management and future policymaking.

Background: Diarrhea remains a common infectious disease caused by various risk factors in developing countries. This study investigated the incidence rate and temporal associations between diarrhea and meteorological determinants in five regions of Surabaya, Indonesia. Method: Monthly diarrhea records from local governmental health facilities in Surabaya and monthly means of weather variables, including average temperature, precipitation, and relative humidity from Meteorology, Climatology, and Geophysical Agency were collected from January 2018 to September 2020. The generalized additive model was employed to quantify the time lag association between diarrhea risk and extremely low (5th percentile) and high (95th percentile) monthly weather variations in the north, central, west, south, and east regions of Surabaya (lag of 0–2 months). Result: The average incidence rate for diarrhea was 11.4 per 100,000 during the study period, with a higher incidence during rainy season (November to March) and in East Surabaya. This study showed that the weather condition with the lowest diarrhea risks varied with the region. The diarrhea risks were associated with extremely low and high temperatures, with the highest RR of 5.39 (95% CI 4.61, 6.17) in the east region, with 1 month of lag time following the extreme temperatures. Extremely low relative humidity increased the diarrhea risks in some regions of Surabaya, with the highest risk in the west region at lag 0 (RR = 2.13 (95% CI 1.79, 2.47)). Extremely high precipitation significantly affects the risk of diarrhea in the central region, at 0 months of lag time, with an RR of 3.05 (95% CI 2.09, 4.01). Conclusion: This study identified a high incidence of diarrhea in the rainy season and in the deficient developed regions of Surabaya, providing evidence that weather magnifies the adverse effects of inadequate environmental sanitation. This study suggests the local environmental and health sectors codevelop a weather-based early warning system and improve local sanitation practices as prevention measures in response to increasing risks of infectious diseases.

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