Influences of environmental factors on biomass of phytoplankton in the northern part of Tai Lake, China, from 2000 to 2012

Associations between climate variables and water quality in low- and middle-income countries: A scoping review

Understanding how climate change will affect water quality and therefore, health, is critical for building resilient water services in low- and middle-income countries (LMICs) where the effect of climate change will be felt most acutely. Evidence of the effect of climate variables such as temperate and rainfall on water quality can generate insights into the likely impact of future climate change. While the seasonal effects on water quality are known, and there is strong qualitative evidence that climate change will impact water quality, there are no reviews that synthesise quantitative evidence from LMICs on links between climate variables and water quality. We mapped the available evidence on a range of climate exposures and water quality outcomes and identified 98 peer-reviewed studies. This included observational studies on the impact of temperature and rainfall events (which may cause short-term changes in contaminant concentrations), and modelling studies on the long-term impacts of sea level rise. Evidence on links between antecedent rainfall and microbiological contamination of water supplies is strong and relatively evenly distributed geographically, but largely focused on faecal indicator bacteria and on untreated shallow groundwater sources of drinking water. The literature on climate effects on geogenic contaminants was sparse. There is substantial research on the links between water temperature and cyanobacteria blooms in surface waters, although most studies were from two countries and did not examine potential effects on water treatment. Similarly, studies modelling the impact of sea level rise on groundwater salinity, mostly from south-Asia and the Middle East, did not discuss challenges for drinking water supplies. We identified key future research priorities based on this review. These include: more studies on specific pathogens (including opportunistic pathogens) in water supplies and their relationships with climate variables; more studies that assess likely relationships between climate variables and water treatment processes; studies into the relationships between climate variables and geogenic contaminants, including risks from heavy metals released as glacier retreat; and, research into the impacts of wildfires on water quality in LMICs given the current dearth of studies but recognised importance.

Eukaryotic community diversity and pathogenic eukaryotes in a full-scale drinking water treatment plant determined by 18S rRNA and metagenomic sequencing

In this study, 18S rRNA high-throughput sequencing was applied to investigate the eukaryotic community in a full-scale drinking water treatment plant. Eukaryotic species and microbial functions in raw water and filter biofilms were identified by metagenomic sequencing. The eukaryotic species richness and diversity presented declining trends throughout the treatment process. The lowest eukaryotic species richness was observed in disinfected water. Arthropoda, Ciliophora, Ochrophyta, and Rotifera were the dominant eukaryotic phyla and exhibited high variations in relative abundance among the different treatment units. Sedimentation significantly decreased the abundance of all eukaryotes except Arthropoda. Biological activated carbon (BAC) filtration and chlorine disinfection exerted strong effects on community composition. The eukaryotic communities in water were distinct from those in filter biofilms, as were the communities of different filter biofilms from each other. In contrast, communities were functionally similar among different filter biofilms, with the category metabolism being the dominant category represented, within which amino acid transport and metabolism (E) and energy production and conversion (C) dominated among subcategories. Seventy-one eukaryotic species pathogenic to humans were identified in raw water and filter biofilms. Quantitative PCR (qPCR) results showed that Acanthamoeba spp. and Vermamoeba vermiformis were present during some treatment processes, with concentrations of 12-1.2 × 10⁵ copies/mL and 1 copy/mL, respectively. Neither of the two pathogenic amoebae was found in disinfected water. Canonical correspondence analysis (CCA) showed that pH was the most important environmental factor affecting eukaryotic community composition. Overall, the results provide insights into the eukaryotic community diversity in drinking water treatment plants and the potential eukaryotic hazards involved in drinking water production.

Effects of climatically-modulated changes in solar radiation and wind speed on spring phytoplankton community dynamics in Lake Taihu, China

Many studies have focused on the interactive effects of temperature increases due to global warming and nutrient enrichment on phytoplankton communities. Recently, non-temperature effects of climate change (e.g., decreases in wind speed and increases in solar radiation) on large lakes have received increasing attention. To evaluate the relative contributions of both temperature and non-temperature effects on phytoplankton communities in a large eutrophic subtropical shallow lake, we analyzed long-term monitoring data from Lake Taihu, China from 1997 to 2016. Results showed that Lake Taihu’s spring phytoplankton biovolume and composition changed dramatically over this time frame, with a change in dominant species. Stepwise multiple linear regression models indicated that spring phytoplankton biovolume was strongly influenced by total phosphorus (TP), light condition, wind speed and total nitrogen (TN) (r_adj² = 0.8, p < 0.01). Partial redundancy analysis (pRDA) showed that light condition accounted for the greatest variation of phytoplankton community composition, followed by TP and wind speed, as well as the interactions between TP and wind speed. Our study points to the additional importance of non-temperature effects of climate change on phytoplankton community dynamics in Lake Taihu.

Substituting values for censored data from Texas, USA, reservoirs inflated and obscured trends in analyses commonly used for water quality target development

We calculated four median datasets (chlorophyll a, Chl a; total phosphorus, TP; and transparency) using multiple approaches to handling censored observations, including substituting fractions of the quantification limit (QL; dataset 1 = 1QL, dataset 2 = 0.5QL) and statistical methods for censored datasets (datasets 3-4) for approximately 100 Texas, USA reservoirs. Trend analyses of differences between dataset 1 and 3 medians indicated percent difference increased linearly above thresholds in percent censored data (%Cen). This relationship was extrapolated to estimate medians for site-parameter combinations with %Cen > 80%, which were combined with dataset 3 as dataset 4. Changepoint analysis of Chl a- and transparency-TP relationships indicated threshold differences up to 50% between datasets. Recursive analysis identified secondary thresholds in dataset 4. Threshold differences show that information introduced via substitution or missing due to limitations of statistical methods biased values, underestimated error, and inflated the strength of TP thresholds identified in datasets 1-3. Analysis of covariance identified differences in linear regression models relating transparency-TP between datasets 1, 2, and the more statistically robust datasets 3-4. Study findings identify high-risk scenarios for biased analytical outcomes when using substitution. These include high probability of median overestimation when %Cen > 50-60% for a single QL, or when %Cen is as low 16% for multiple QL's. Changepoint analysis was uniquely vulnerable to substitution effects when using medians from sites with %Cen > 50%. Linear regression analysis was less sensitive to substitution and missing data effects, but differences in model parameters for transparency cannot be discounted and could be magnified by log-transformation of the variables.