Evaluation of porcine decomposition and total body score (TBS) in a central European temperate forest

The total body score (TBS) is a visual scoring method to scale the succession of decomposition stages. It compares decomposition between cadavers, to connect it with external taphonomic factors and estimate the post-mortem interval. To study decomposition in various climatic environments, pigs are often used as human proxies. Currently, there is one TBS system by Keough et al. (J Forensic Sci. 2017;62:986) for surface-deposited domestic pigs, coming from South Africa. Our study aims to evaluate this method and analyze porcine decomposition in Central Europe to inform forensic research and casework. We conducted an experiment studying six 50 kg pig carcasses in a temperate Swiss forest. Three observers documented decomposition patterns and rated the decomposition stages from photographs based on the porcine TBS model by Keough et al. (J Forensic Sci. 2017;62:986). We documented discrepancies between the carcass decomposition of our specimens and those in the South African study, especially related to the high insect activity in our experiment. Furthermore, we noted factors complicating TBS scoring, including rainfall and scavengers. The agreement between TBS observers from photographs was in the highest agreement category apart from one "substantial agreement" category. Our study is the first in Europe to systematically test the Keough et al. (J Forensic Sci. 2017;62:986) method. The results evidence that regional adaptations are required to be applicable for other environments. We present a modified approach based on experimental observations in a Swiss temperate forest. The identification of regional decomposition patterns and drivers will inform future taphonomy research as well as forensic casework in comparable contexts in Central Europe.

Streamflow seasonality in a snow-dwindling world

Climate warming induces shifts from snow to rain in cold regions1, altering snowpack dynamics with consequent impacts on streamflow that raise challenges to many aspects of ecosystem services2-4. A straightforward conceptual model states that as the fraction of precipitation falling as snow (snowfall fraction) declines, less solid water is stored over the winter and both snowmelt and streamflow shift earlier in season. Yet the responses of streamflow patterns to shifts in snowfall fraction remain uncertain5-9. Here we show that as snowfall fraction declines, the timing of the centre of streamflow mass may be advanced or delayed. Our results, based on analysis of 1950-2020 streamflow measurements across 3,049 snow-affected catchments over the Northern Hemisphere, show that mean snowfall fraction modulates the seasonal response to reductions in snowfall fraction. Specifically, temporal changes in streamflow timing with declining snowfall fraction reveal a gradient from earlier streamflow in snow-rich catchments to delayed streamflow in less snowy catchments. Furthermore, interannual variability of streamflow timing and seasonal variation increase as snowfall fraction decreases across both space and time. Our findings revise the 'less snow equals earlier streamflow' heuristic and instead point towards a complex evolution of seasonal streamflow regimes in a snow-dwindling world.

Meta-analysis reveals that the effects of precipitation change on soil and litter fauna in forests depend on body size

Anthropogenic climate change is altering precipitation regimes at a global scale. While precipitation changes have been linked to changes in the abundance and diversity of soil and litter invertebrate fauna in forests, general trends have remained elusive due to mixed results from primary studies. We used a meta-analysis based on 430 comparisons from 38 primary studies to address associated knowledge gaps, (i) quantifying impacts of precipitation change on forest soil and litter fauna abundance and diversity, (ii) exploring reasons for variation in impacts and (iii) examining biases affecting the realism and accuracy of experimental studies. Precipitation reductions led to a decrease of 39% in soil and litter fauna abundance, with a 35% increase in abundance under precipitation increases, while diversity impacts were smaller. A statistical model containing an interaction between body size and the magnitude of precipitation change showed that mesofauna (e.g. mites, collembola) responded most to changes in precipitation. Changes in taxonomic richness were related solely to the magnitude of precipitation change. Our results suggest that body size is related to the ability of a taxon to survive under drought conditions, or to benefit from high precipitation. We also found that most experiments manipulated precipitation in a way that aligns better with predicted extreme climatic events than with predicted average annual changes in precipitation and that the experimental plots used in experiments were likely too small to accurately capture changes for mobile taxa. The relationship between body size and response to precipitation found here has far-reaching implications for our ability to predict future responses of soil biodiversity to climate change and will help to produce more realistic mechanistic soil models which aim to simulate the responses of soils to global change.

Climate velocities and species tracking in global mountain regions

Mountain ranges contain high concentrations of endemic species and are indispensable refugia for lowland species that are facing anthropogenic climate change1,2. Forecasting biodiversity redistribution hinges on assessing whether species can track shifting isotherms as the climate warms3,4. However, a global analysis of the velocities of isotherm shifts along elevation gradients is hindered by the scarcity of weather stations in mountainous regions5. Here we address this issue by mapping the lapse rate of temperature (LRT) across mountain regions globally, both by using satellite data (SLRT) and by using the laws of thermodynamics to account for water vapour6 (that is, the moist adiabatic lapse rate (MALRT)). By dividing the rate of surface warming from 1971 to 2020 by either the SLRT or the MALRT, we provide maps of vertical isotherm shift velocities. We identify 17 mountain regions with exceptionally high vertical isotherm shift velocities (greater than 11.67 m per year for the SLRT; greater than 8.25 m per year for the MALRT), predominantly in dry areas but also in wet regions with shallow lapse rates; for example, northern Sumatra, the Brazilian highlands and southern Africa. By linking these velocities to the velocities of species range shifts, we report instances of close tracking in mountains with lower climate velocities. However, many species lag behind, suggesting that range shift dynamics would persist even if we managed to curb climate-change trajectories. Our findings are key for devising global conservation strategies, particularly in the 17 high-velocity mountain regions that we have identified.

Parameterization of nutrients and sediment build-up/wash-off processes for simulating stormwater quality from specific land uses

Urbanization changes land cover through the expansion of impermeable surfaces, leading to a significant rise in runoff, sediment, and nutrient loading. The quality of stormwater is related to land use and is highly variable. Currently, stormwater is predominantly described through watershed models that rely minimally, if at all, on field monitoring data. The simple event mean concentration (EMC) wash-off approach by land use is a common method for estimating urban runoff loads. However, a major drawback of the EMC approach is it assumes concentration remains constant across events for a specific land use. Build-up/wash-off equations have been formulated to consider variations in concentration between events. However, several equation parameters are challenging to estimate, making them difficult to use. We conducted a monitoring and modeling study and investigated the impact of land use on stormwater quantity and quality and optimized and investigated the build-up/wash-off parameters for three homogenous urban land uses to estimate nutrients (nitrogen and phosphorus) and sediment loads. Stormwater from commercial, medium-density residential, and transportation land uses was sampled using automatic samplers during storm events, and water quality was characterized for a variety of them for 14 months. Analysis of stormwater samples included assessments for total nitrogen, total phosphorus, and total suspended solids. Results showed that medium-density residential land use had the highest median total nitrogen and total phosphorus event mean concentrations and commercial had the highest median total suspended solids EMCs. Water quality parameters (or build-up/wash-off parameters) exhibited significant variation between land uses, confirming that land use is a key determinant of stormwater quality. The median particle size for each land use was less than 150 μm, indicating that the most common particle size in stormwater was a very fine sand or smaller. This small size should be considered by stakeholders in the design of stormwater treatment systems.

Wildland-urban interface wildfire increases metal contributions to stormwater runoff in Paradise, California

The 2018 Camp Fire was a large late-year (November) wildfire that produced an urban firestorm in the Town of Paradise, California, USA, and destroyed more than 18 000 structures. Runoff from burned wildland areas is known to contain ash, which can transport contaminants including metals into nearby watersheds. However, due to historically infrequent occurrences, the effect of wildland-urban interface (WUI) fires, such as the Camp Fire, on surface water quality has not been well-characterized. Therefore, this study investigated the effects of widespread urban burning on surface water quality in major watersheds of the Camp Fire area. Between November 2018 and May 2019, 140 surface water samples were collected, including baseflow and stormflow, from burned and unburned watersheds with varying extent of urban development. Samples were analyzed for total and filter-passing metals, dissolved organic carbon, major anions, and total suspended solids. Ash and debris from the Camp Fire contributed metals to downstream watersheds via runoff throughout the storm season. Increases in concentration up to 200-fold were found for metals Cr, Cu, Ni, Pb, and Zn in burned watersheds compared to pre-fire values. Total concentrations of Al, Cd, Cu, Pb, and Zn exceeded EPA aquatic habitat acute criteria by up to 16-fold for up to five months after the fire. To assess possible transport mechanisms and bioavailability, a subset of 18 samples was analyzed using four filters with nominal pore sizes ranging from 0.22 to 1.2 μm to determine the particulate size distribution of metals. Trace and major metals (Al, Ba, Co, Cr, Cu, Fe, Hg, Mn, Ni, Pb, and Zn) were found mostly associated with larger grain sizes (>0.45 μm), and some metals (Al, Cr, Fe, and Pb) also included a substantial colloidal phase (0.22 to 0.45 μm). This study suggests that fires in the wildland-urban interface increase metal concentrations, mainly through particulate driven transport. The metals with the largest increases are likely from anthropogenic disaster materials, though biomass ash also is a major contributor to water quality. The increase in metals following WUI burning may have adverse ecological impacts.

Warming and altered precipitation independently and interactively suppress alpine soil microbial growth in a decadal-long experiment

Warming and precipitation anomalies affect terrestrial carbon balance partly through altering microbial eco-physiological processes (e.g., growth and death) in soil. However, little is known about how such processes responds to simultaneous regime shifts in temperature and precipitation. We used the 18O-water quantitative stable isotope probing approach to estimate bacterial growth in alpine meadow soils of the Tibetan Plateau after a decade of warming and altered precipitation manipulation. Our results showed that the growth of major taxa was suppressed by the single and combined effects of temperature and precipitation, eliciting 40-90% of growth reduction of whole community. The antagonistic interactions of warming and altered precipitation on population growth were common (~70% taxa), represented by the weak antagonistic interactions of warming and drought, and the neutralizing effects of warming and wet. The members in Solirubrobacter and Pseudonocardia genera had high growth rates under changed climate regimes. These results are important to understand and predict the soil microbial dynamics in alpine meadow ecosystems suffering from multiple climate change factors.

Biochar and vegetation effects on discharge water quality from organic-substrate green roofs

Green roofs have been increasingly used to improve stormwater management, but poor vegetation performance on roof systems, varying with vegetation type, can degrade discharge quality. Biochar has been suggested as an effective substrate additive for green roofs to improve plant performance and discharge quality. However, research on the effects of biochar and vegetation on discharge quality in the long term is lacking and the underlying mechanisms involved are unclear. We examined the effects of biochar amendment and vegetation on discharge quality on organic-substrate green roofs with pre-grown sedum mats and direct-seeded native plants for three years and investigated the key factors influencing discharge quality. Sedum mats reduced the leaching of nutrients and particulate matter by 6-64% relative to native plants, largely due to the higher initial vegetation cover of the former. Biochar addition to sedum mat green roofs resulted in the best integrated water quality due to enhanced plant cover and sorption effects. Structural equation modeling revealed that nutrient leaching was primarily influenced by rainfall depth, time, vegetation cover, and substrate pH. Although biochar-amended sedum mats showed better discharge quality from organic-substrate green roofs, additional ecosystem services may be provided by native plants, suggesting future research to optimize plant composition and cover and biochar properties for sustainable green roofs.

Survival and rapid resuscitation permit limited productivity in desert microbial communities

Microbial activity in drylands tends to be confined to rare and short periods of rain. Rapid growth should be key to the maintenance of ecosystem processes in such narrow activity windows, if desiccation and rehydration cause widespread cell death due to osmotic stress. Here, simulating rain with 2H2O followed by single-cell NanoSIMS, we show that biocrust microbial communities in the Negev Desert are characterized by limited productivity, with median replication times of 6 to 19 days and restricted number of days allowing growth. Genome-resolved metatranscriptomics reveals that nearly all microbial populations resuscitate within minutes after simulated rain, independent of taxonomy, and invest their activity into repair and energy generation. Together, our data reveal a community that makes optimal use of short activity phases by fast and universal resuscitation enabling the maintenance of key ecosystem functions. We conclude that desert biocrust communities are highly adapted to surviving rapid changes in soil moisture and solute concentrations, resulting in high persistence that balances limited productivity.

Removal and release of microplastics and other environmental pollutants during the start-up of bioretention filters treating stormwater

Untreated stormwater is a major source of microplastics, organic pollutants, metals, and nutrients in urban water courses. The aim of this study was to improve the knowledge about the start-up periods of bioretention filters. A rain garden pilot facility with 13 bioretention filters was constructed and stormwater from a highway and adjacent impervious surfaces was used for irrigation for ∼12 weeks. Selected plants (Armeria maritima, Hippophae rhamnoides, Juncus effusus, and Festuca rubra) was planted in ten filters. Stormwater percolated through the filters containing waste-to-energy bottom ash, biochar, or Sphagnum peat, mixed with sandy loam. Influent and effluent samples were taken to evaluate removal of the above-mentioned pollutants. All filters efficiently removed microplastics >10 µm, organic pollutants, and most metals. Copper leached from all filters initially but was significantly reduced in the biochar filters at the end of the period, while the other filters showed a declining trend. All filters leached nutrients initially, but concentrations decreased over time, and the biochar filters had efficiently reduced nitrogen after a few weeks. To conclude, all the filters effectively removed pollutants during the start-up period. Before being recommended for full-scale applications, the functionality of the filters after a longer period of operation should be evaluated.

Characterization of road-deposited sediment wash-off and accurate splitting of initial runoff pollution in heterogeneous urban spaces

Particulate materials arising from road-deposited sediments (RDS) are an essential target for the control and management of surface runoff pollution. However, the heterogeneity of urban spaces hinders the identification and quantification of particulate pollution, which is challenging when formulating precise control measures. To elucidate the factors that drive particulate pollution in heterogeneous urban spaces, the accumulation of RDS on dry days and the total suspended solids during six natural rainfall events were investigated across three urban-rural spatial units (central urban, central suburban, and remote suburban). The underlying surface type (asphalt or cement roads) and particle size composition jointly determined the spatial heterogeneity in the static accumulation and dynamic output loads of RDS during rainfall. These two factors explained 59.6% and 18.9% of the spatial heterogeneity, respectively, according to principal component analysis. A novel CPSI exponential wash-off equation that incorporates particle size composition and underlying surface type was applied. It precisely described the spatial heterogeneity of RDS wash-off loads, the estimated values exhibiting event mean concentration errors of 10.8-18.2%. When coupled with the M(V) curve, this CPSI exponential wash-off equation more precisely split the initial volume of runoff: a lower total volume (17.6-38.0%) was shown to carry a higher proportion of the load (70.0-93.7%) compared to the traditional coupled exponential wash-off equation (volume: 31.6-49.0%, load: 37-90%). This study provides a new approach to characterizing RDS wash-off processes and splitting initial runoff in heterogeneous spaces.

A typology of subseasonal rainfall evolution during the southern Niger monsoon

Niger is highly vulnerable to rainfall variability, often with adverse socioeconomic consequences. This study examined observed subseasonal rainfall variability during Niger's monsoon season (May to September). Using k-means clustering of dekadal (ten-day) rainfall, a typology was developed for the annual evolution of the monsoon season. Year-to-year rainfall variability for each of the first few dekads of the season is modest, but the middle, or peak of the rainy season demonstrates large interannual variability. Clustering analysis of annual timeseries for each dekad of the season revealed two types of monsoon progression. The distinction between the two types is strongly dependent on differences during the latter half of the season. For the first and third ten-day periods in August, and the first ten days in September, the two groups of years are more distinct. These results imply that while reliable prediction of the timing of anomalous onsets will be challenging, due to the relatively narrow range of uncertainty historically, there are opportunities for further exploration of dynamic and or statistical predictors or precursors using this typology that could potentially provide better information for decision-makers, especially with respect to agriculture.

Exploring the adoption of water quality trading as an alternative stormwater regulatory compliance strategy for land development projects: A case study for Roanoke, Virginia

Urban stormwater runoff is a significant source of nutrient pollution that is very costly to treat. Water quality trading (WQT) is a market-based strategy that can be used to lower the costs associated with meeting stormwater quality regulations. While many WQT programs have experienced low participation, Virginia's program has seen high participation due to the inclusion of land developers and other regulated stormwater dischargers. However, the extent to which WQT is used as a compliance option by regulated stormwater dischargers is not well understood, particularly when compared with the adoption of traditional compliance options. To address this knowledge gap, we collated a novel dataset comprising site characteristics and stormwater compliance methods for all development projects in the City of Roanoke, Virginia from December 2015 to March 2022. We analyzed this dataset to characterize the adoption of nutrient offset credits and other compliance methods being used, including best management practices (BMPs) and improved land covers associated with reduced nutrient export. Results show that credits are the preferred compliance option in Roanoke and were used as the only treatment compliance method for 59% of projects with treatment requirements. Projects using credits corresponded with a lower median disturbed area (1.36 acres) and lower median nutrient load reduction requirement (0.69 pounds of total phosphorus per year) compared with other compliance methods. Furthermore, we found that 58% of the projects that used credits achieved stormwater quantity compliance using methods other than implementing stormwater control devices. By mapping buyers and sellers of credits, we found that all credit sellers are downstream of the development projects. We discuss how this downstream trading could be a cause for concern, as part of a larger discussion of the advantages of tracking stormwater compliance methods, drawing on Roanoke as a case study.

Study loss of vegetative cover and increased land surface temperature through remote sensing strategies under the inter-annual climate variability in Jinhua-Quzhou basin, China

The Jinhua-Quzhou basin in China is one of the most susceptible areas to drought. Due to the loss of vegetation and great fluctuations in rainfall and surface temperature, global warming occurs. Timely, accurate, and effective drought monitoring is crucial for protecting local vegetation and determining which vegetation is most vulnerable to increased LST during the period 1982-2019. It assumes a strong correlation between loss of vegetation cover, changes in monsoon climate, drought, and increases in land surface temperature (LST). Due to significantly increased in LST, low precipitation and vegetation cover, NDVI, TVDI, VCI, and NAP are useful in characterizing drought mitigation strategies. The temperature vegetation drought index (TVDI), normalized difference vegetation index (NDVI), vegetation condition index (VCI), and monthly precipitation anomaly percentage (NAP) can be helped to characterize drought reduction strategies. Monthly NDVI, NAP, VCI, TVDI, normalized vegetation supply water index (NVSWI), temperature condition index (TCI), vegetation health index (VHI), and heat map analysis indicate that the Jinhua-Quzhou basin experienced drought during 1984, 1993, 2000, and 2011. Seasonal SR, WVP, WS, NDVI, VCI, and NAP charts confirm that the Jinhua-Quzhou basin was affected by severe drought in 1984, which continued and led to severe droughts in 1993, 2000, and 2011. Regression analysis showed a significant positive correlation between NDVI, TVDI, VCI, and NAP values, while NVSWI, TVDI, and VHI showed positive signs of good drought monitoring strategies. The research results confirm the correlation between loss of vegetation cover and LST, which is one of the causes of global warming. The distribution of drought changed a trend indicating that compared with the Jinhua region; the Quzhou region has more droughts. The changing trend of drought has characteristics from 1982 to 2019, and there are significant differences in drought changing trends between different Jinhua-Quzhou basin areas. Overall, from 1982 to 2019, the frequency of drought showed a downward trend. We believe that these results will provide useful tools for drought management plans and play a relevant role in mitigating the effects of drought and protecting humanity from climate hazards.

Coupling simulation of pipeline nodes - Storage tank linkage in urban high-density built-up areas using optimization model

Climate change and urbanization contribute to the increased frequency of short-duration intense rainstorms. Traditional solutions often involve multiple scenarios for cost-effectiveness comparison, neglecting the rationality of placement conditions. The effective coupling and coordination of the location, number, size, and cost of storage tanks are crucial to addressing this issue. A three-phase approach is proposed to enhance the dynamic link between drainage pipeline and storage tanks in urban high-density built-up areas, integrating Python language, SWMM, the Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-III), and the Analytic Hierarchy Process (AHP) methods. In the first stage, each node within the pipeline network is considered as a potential storage tank location. In the second stage, factors such as the length and diameter of the upstream connecting pipeline, as well as the suitability of the storage tank location, are assessed. In the third stage, the length and diameter of the downstream connecting pipeline node are evaluated. The results show that the 90 overflow nodes (overflow time >0.5h) have been cleared using the three-phase approach with a 50a (duration = 3h) return period as the rainfall scenario, which meets the flooding limitations. After the completion of the three-phase method configuration, the total overflow and SS loads were reduced by 96.45% and 49.30%, respectively, compared to the status quo conditions. These two indicators have decreased by 48.16 and 9.05%, respectively, compared to the first phase (the traditional method of only replacing all overflow nodes with storage tanks). The proposed framework enables decision-makers to evaluate the acceptability and reliability of the optimal management plan, taking into account their preferences and uncertainties.

Assessment of rainfall-derived inflow and infiltration in sewer systems with machine learning approaches

Rainfall-derived inflow/infiltration (RDII) modelling during heavy rainfall events is essential for sewer flow management. In this study, two machine learning algorithms, random forest (RF) and long short-term memory (LSTM), were developed for sewer flow prediction and RDII estimation based on field monitoring data. The study implemented feature engineering for extracting physically significant features in sewer flow modelling and investigated the importance of the relevant features. The results from two case studies indicated the superior capability of machine learning models in RDII estimation in the combined and separated sewer systems, and LSTM model outperformed the two models. Compared to traditional methods, machine learning models were capable of simulating the temporal variation in RDII processes and improved prediction accuracy for peak flows and RDII volumes in storm events.

Comparison of the performance of SWAT and hybrid M5P tree models in rainfall-runoff simulation

Stream flow forecasting is a crucial aspect of hydrology and water resource management. This study explores stream flow forecasting using two distinct models: the Soil and Water Assessment Tool (SWAT) and a hybrid M5P model tree. The research specifically targets the daily stream flow predictions at the MH Halli gauge stations, located along the Hemvati River in Karnataka, India. A 14-year dataset spanning from 2003 to 2017 is divided into two subsets for model calibration and validation. The SWAT model's performance is evaluated by comparing its predictions to observed stream flow data. Residual time series values resulting from this comparison are then resolved using the M5P model tree. The findings reveal that the hybrid M5P tree model surpasses the SWAT model in terms of various evaluation metrics, including root-mean-square error, coefficient of determination (R2), Nash-Sutcliffe efficiency, and degree of agreement (d) for the MH Halli stations. In conclusion, this study shows the effectiveness of the hybrid M5P tree model in stream flow forecasting. The research contributes valuable insights into improved water resource management and underscores the importance of selecting appropriate models based on their performance and suitability for specific hydrological forecasting tasks.

Role of Rain in the Spore Dispersal of Fungal Pathogens Associated with Grapevine Trunk Diseases

Grapevine trunk diseases are caused by a complex of fungi that belong to different taxa, which produce different spore types and have different spore dispersal mechanisms. It is commonly accepted that rainfall plays a key role in spore dispersal, but there is conflicting information in the literature on the relationship between rain and spore trapping in aerobiology studies. We conducted a systematic literature review, extracted quantitative data from published papers, and used the pooled data for Bayesian analysis of the effect of rain on spore trapping. We selected 17 papers covering 95 studies and 8,778 trapping periods, concerning a total of 26 fungal taxa causing Botryosphaeria dieback (BD), Esca complex (EC), and Eutypa dieback (ED). Results confirmed the role of rain in the spore dispersal of these fungi but revealed differences among the different fungi. Rain was a good predictor of spore trapping for ED (AUROC = 0.820) and BD (0.766) but not for the ascomycetes involved in EC (0.569) and not for the only basidiomycetes, Fomitiporella viticola, studied as for spore discharge (AUROC not significant). Prediction of spore trapping was more accurate for negative prognosis than for positive prognosis; a rain cutoff of ≥0.2 mm provided an overall accuracy of ≥0.61 for correct prognoses. Spores trapped in rainless periods accounted for only <10% of the total spores. Our analysis had some drawbacks, which were mainly caused by knowledge gaps and limited data availability; these drawbacks are discussed to facilitate further research.

Removal effect of pollutants from stormwater runoff in shallow bioretention system with gramineous plants

The bioretention system is one of the most widely used low impact development (LID) facilities with efficient purification capacity for stormwater, and its planting design has been a hot spot for research at home and abroad. In this paper, ryegrass (Lolium perenne L.), bermuda (Cynodon dactylon Linn.), bahiagrass (Paspalum notatum Flugge), and green grass (Cynodon dactylon × C .transadlensis 'Tifdwarf') were chosen as plant species to construct a shallow bioretention system. The growth traits and nutrient absorption ability of four gramineous plants were analyzed. Their tolerance, enrichment, and transportation capacity were also evaluated to compare plant species and their absorptive capacity of heavy metals (Cu, Pb, and Zn). Results showed that the maximum absorption rate (Imax) ranged from 22.1 to 42.4 μg/(g·h) for P and ranged from 65.4 to 104.8 μg/(g·h) for NH4+-N; ryegrass had the strongest absorption capacity for heavy metals and the maximum removal rates of Cu, Pb, and Zn by four grasses were 78.4, 59.4, and 51.3%, respectively; the bioretention cell with ryegrass (3#) was significantly more effective in purifying than the unplanted bioretention cell (1#) during the simulated rainfall test. Overall, the system parameters were optimized to improve the technical application of gramineous plants in the bioretention system.

Microbial communities exhibit distinct diversities and assembly mechanisms in rainwater and tap-water storage systems

Roof-harvested rainwater stored for potable and nonpotable usages represent a clean and sustainable water supply resource. However, the microbial dynamics and mechanisms of community assembly in long-termed operated rainwater storage systems remain elusive. In this study, characteristics of microbial communities in different habitats were systematically compared within rainwater and tap-water simulated storage systems (SWSSs) constructed with different tank materials (PVC, stainless steel and cement). Distinct microbial communities were observed between rainwater and tap-water SWSSs for both water and biofilm samples (ANOSIM, p < 0.05), with lower diversity indexes noted in rainwater samples. Notably, a divergent potential pathogen profile was observed between rainwater and tap-water SWSSs, with higher relative abundances of potential pathogens noted in rainwater SWSSs. Moreover, tank materials had a notable impact on microbial communities in rainwater SWSSs (ANOSIM, p < 0.05), rather than tap-water SWSSs, illustrating the distinct interplay between water chemistry and engineering factors in shaping the SWSS microbiomes. Deterministic processes contributed predominantly to the microbial community assembly in cement rainwater SWSSs and all tap-water SWSSs, which might be ascribed to the high pH levels in cement rainwater SWSSs and low-nutrient levels in all tap-water SWSSs, respectively. However, microbial communities in the PVC and stainless-steel rainwater SWSSs were mainly driven by stochastic processes. Overall, the results provided insights to the distinct microbial assembly mechanisms and potential health risks in stored roof-harvested rainwater, highlighting the importance of developing tailored microbial management strategies for the storage and utilization of rainwater.

Do baseline assumptions alter the efficacy of green stormwater infrastructure to reduce combined sewer overflows?

Green stormwater infrastructure (GSI) is growing in popularity to reduce combined sewer overflows (CSOs) and hydrologic simulation models are a tool to assess their reduction potential. Given the numerous and interacting water flows that contribute to CSOs, such as evapotranspiration (ET) and groundwater (GW), these models should ideally account for them. However, due to the complexity, simplified models are often used, and it is currently unknown how these assumptions affect estimates of CSOs, GSI effectiveness, and ultimately planning guidance. This study evaluates the effect on estimates of CSOs and GSI effectiveness when different flows and hydrologic processes are neglected. We modified an existing EPA SWMM model of a combined sewer system in Switzerland to include ET, GW, and upstream inflows. Historical rainfall data over 30 years are used to assess volume and duration of CSOs with and without three types of GSI (bioretention basins, permeable pavements and green roofs). Results demonstrate that neglect of certain flows in modelling can alter CSO volumes from -15 % to 40 %. GSI effectiveness also varies considerably, resulting in differences in simulated percent of CSO volume reduced from 8 % to 35 %, depending on the GSI type and modeled flow or process. Representation of GW within models is particularly crucial when infiltrating GSI are present, as CSOs could increase in certain subcatchments due to higher GW levels from increased infiltration. When basing GSI planning decisions on modeled estimates of CSOs, all relevant hydrologic processes should be included to the extent possible, and uncertainty and assumptions should always be considered.

Temporal variations in micropollutant inlet concentrations matter when planning the design and compliance assessment of stormwater control measures

Stormwater Control Measures (SCMs) contribute to reducing micropollutant emissions from separate sewer systems. SCM planning and design are often performed by looking at the hydrological performance. Assessment of pollutant removal and the ability to comply with discharge concentration limits is often simplified due to a lack of data and limited monitoring resources. This study analyses the impact of using different time resolutions of input stormwater concentrations when assessing the compliance of SCMs against water quality standards. The behaviour of three indicator micropollutants (MP - Copper, Diuron, Benzo[a]pyrene) was assessed in four SCM archetypes, which were defined to represent typical SCM removal processes. High resolution MP data were extrapolated by using high resolution (2 min) measurements of TSS over a long period (343 events). The compliance assessment showed that high resolution input concentrations can result in a different level of compliance with water quality standards, especially when discharged concentrations are close to the limit values. This study underlines the importance of considering the high temporal variability of stormwater micropollutants when planning and designing SCMs to identify the most effective solutions for stormwater pollution management and to ensure a thorough consideration of all the environmental implications.

Projection of dengue fever transmissibility under climate change in South and Southeast Asian countries

Vector-borne infectious disease such as dengue fever (DF) has spread rapidly due to more suitable living environments. Considering the limited studies investigating the disease spread under climate change in South and Southeast Asia, this study aimed to project the DF transmission potential in 30 locations across four South and Southeast Asian countries. In this study, weekly DF incidence data, daily mean temperature, and rainfall data in 30 locations in Singapore, Sri Lanka, Malaysia, and Thailand from 2012 to 2020 were collected. The effects of temperature and rainfall on the time-varying reproduction number (Rt) of DF transmission were examined using generalized additive models. Projections of location-specific Rt from 2030s to 2090s were determined using projected temperature and rainfall under three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585), and the peak DF transmissibility and epidemic duration in the future were estimated. According to the results, the projected changes in the peak Rt and epidemic duration varied across locations, and the most significant change was observed under middle-to-high greenhouse gas emission scenarios. Under SSP585, the country-specific peak Rt was projected to decrease from 1.63 (95% confidence interval: 1.39-1.91), 2.60 (1.89-3.57), and 1.41 (1.22-1.64) in 2030s to 1.22 (0.98-1.51), 2.09 (1.26-3.47), and 1.37 (0.83-2.27) in 2090s in Singapore, Thailand, and Malaysia, respectively. Yet, the peak Rt in Sri Lanka changed slightly from 2030s to 2090s under SSP585. The epidemic duration in Singapore and Malaysia was projected to decline under SSP585. In conclusion, the change of peak DF transmission potential and disease outbreak duration would vary across locations, particularly under middle-to-high greenhouse gas emission scenarios. Interventions should be considered to slow down global warming as well as the potential increase in DF transmissibility in some locations of South and Southeast Asia.

Bioretention Design Modifications Increase the Simulated Capture of Hydrophobic and Hydrophilic Trace Organic Compounds

Stormwater rapidly moves trace organic contaminants (TrOCs) from the built environment to the aquatic environment. Bioretention cells reduce loadings of some TrOCs, but they struggle with hydrophilic compounds. Herein, we assessed the potential to enhance TrOC removal via changes in bioretention system design by simulating the fate of seven high-priority stormwater TrOCs (e.g., PFOA, 6PPD-quinone, PAHs) with log KOC values between -1.5 and 6.74 in a bioretention cell. We evaluated eight design and management interventions for three illustrative use cases representing a highway, a residential area, and an airport. We suggest two metrics of performance: mass advected to the sewer network, which poses an acute risk to aquatic ecosystems, and total mass advected from the system, which poses a longer-term risk for persistent compounds. The optimized designs for each use case reduced effluent loadings of all but the most polar compound (PFOA) to <5% of influent mass. Our results suggest that having the largest possible system area allowed bioretention systems to provide benefits during larger events, which improved performance for all compounds. To improve performance for the most hydrophilic TrOCs, an amendment like biochar was necessary; field-scale research is needed to confirm this result. Our results showed that changing the design of bioretention systems can allow them to effectively capture TrOCs with a wide range of physicochemical properties, protecting human health and aquatic species from chemical impacts.

Responses of runoff and sediment yield to slope length and gravel content of Lou soil engineering accumulation slope in Guanzhong region, Northwest China

With the economic development, a large number of engineering accumulation bodies with Lou soil as the main soil type were produced in Guanzhong area, Northwest China. We examined the characteristics of runoff and sediment yield of Lou soil accumulation bodies with earth (gravel content 0%) and earth-rock (gravel content 30%) under different rainfall intensities (1.0, 1.5, 2.0, 2.5 mm·min-1) and different slope lengths (3, 5, 6.5, 12 m) by the simulating rainfall method. The results showed that runoff rate was relatively stable when rainfall intensity was 1.0-1.5 mm·min-1, while runoff rate fluctuated obviously when rainfall intensity was 2.0-2.5 mm·min-1. The average runoff rate varied significantly across different rainfall intensities on the same slopes, and the difference of average runoff rate of the two slopes was significantly increased with rainfall intensity. Under the same rainfall intensity, the difference in runoff rate between the slope lengths of the earth-rock slope was more obvious than that of the earth slope. When the slope length was 3-6.5 m, flow velocity increased rapidly at first and then increased slowly or tended to be stable. When the slope length was 12 m, flow velocity increased significantly. In general, with the increases of rainfall intensity, inhibition effect of gravel on the average flow velocity was enhanced. When rainfall intensity was 2.5 mm·min-1, the maximum reduction in the average flow velocity of earth-rock slope was 61.5% lower than that of earth slope. When rainfall intensity was less than 2.0 mm·min-1, sediment yield rate showed a trend of gradual decline or stable change, while that under the other rainfall intensities showed a trend of rapid decline and then fluctuated sharply. The greater the rainfall intensity, the more obvious the fluctuation. There was a significant positive correlation between the average sediment yield rate and runoff parameters, with the runoff rate showing the best fitting effect. Among the factors, slope length had the highest contribution to runoff velocity and rainfall erosion, which was 51.8% and 35.5%, respectively. This study can provide scientific basis for soil and water erosion control of engineering accumulation in Lou soil areas.

Climate change impacts on rainfall intensity-duration-frequency curves in local scale catchments

The increasing intensity and frequency of rainfall events, a critical aspect of climate change, pose significant challenges in the construction of intensity-duration-frequency (IDF) curves for climate projection. These curves are crucial for infrastructure development, but the non-stationarity of extreme rainfall raises concerns about their adequacy under future climate conditions. This research addresses these challenges by investigating the reasons behind the IPCC climate report's evidence about the validity that rainfall follows the Clausius-Clapeyron (CC) relationship, which suggests a 7% increase in precipitation per 1 °C increase in temperature. Our study provides guidelines for adjusting IDF curves in the future, considering both current and future climates. We calculate extreme precipitation changes and scaling factors for small urban catchments in Barranquilla, Colombia, a tropical region, using the bootstrapping method. This reveals the occurrence of a sub-CC relationship, suggesting that the generalized 7% figure may not be universally applicable. In contrast, our comparative analysis with Illinois, USA, an inland city in the north temperate zone, shows adherence to the CC relationship. This emphasizes the need for local parameter calculations rather than relying solely on the generalized 7% figure.

HYADES - A Global Archive of Annual Maxima Daily Precipitation

Time series of annual maxima daily precipitation are crucial for understanding extreme precipitation behavior and its shifts toward nonstationarity with global warming. Extreme precipitation insight assists hydraulic infrastructure design, water resource management, natural hazard prevention, and climate change adaptation. However, not even a third of the records are of sufficient length, and the number of active stations keeps decreasing. Herein, we present HYADES: archive of yearly maxima of daily precipitation records, a global dataset derived from the Global Historical Climatology Network database of daily records (GHCN-Daily). The HYADES dataset contains records from 39 206 stations (heterogeneously distributed worldwide) with record lengths varying from 16 to 200 years between 1805 and 2023. HYADES was extracted through a methodology designed to accurately capture the true maxima even in the presence of missing values within the records. The method's thresholds were determined and evaluated through Monte Carlo simulations. Our approach demonstrates a 96.73% success rate in detecting the true maxima while preserving time series statistical properties of interest (L-moments and temporal monotonic trend).

Effect of vegetation treatment and water stress on evapotranspiration in bioretention systems

Evapotranspiration is a key hydrological process for reducing stormwater runoff in bioretention systems, regardless of their physical configuration. Understanding the volumes of stormwater that can be returned to the atmosphere via evapotranspiration is, therefore, a key consideration in the design of any bioretention system. This study establishes the evapotranspiration dynamics of three common, structurally different, bioretention vegetation treatments (an Amenity Grass mix, and mono-cultures of Deschampsia cespitosa and Iris sibirica) compared with an un-vegetated control using lab-scale column experiments. Via continuous mass and moisture loss data, observed evapotranspiration rates were compared with those predicted by the FAO-56 Penman-Monteith model for five 14-day dry periods during Spring 2021, Summer 2021, and Spring 2022. Soil moisture reductions over the 14-day trials led to reduced rates of evapotranspiration. This necessitated the use of a soil moisture extraction function alongside a crop coefficient to represent actual evapotranspiration from FAO-56 Penman-Monteith reference evapotranspiration estimates. Crop coefficients (Kc) varied between 0.65 and 2.91, with a value of 1.0 identified as a recommended default value in the absence of treatment-specific empirical data. A continuous hydrological model with Kc=1.0 and a loading ratio of 10:1 showed that evapotranspiration could account for between 1 and 12% of the annual water budget for a bioretention system located in the UK and Ireland, increasing to a maximum of 35% when using the highest Kc observed (2.91).

Efficiency of fluralaner pour-on in different strategic control protocols against Rhipicephalus microplus on Brangus cattle in a tropical area

BACKGROUND

The occurrence of higher winter temperatures in Brazilian areas with tropical and highland climates may result in a fifth peak of tick populations during winter in addition to the four generations previously described. Therefore, a strategic control protocol was developed with treatments in two seasons with the objective of controlling the generations of ticks that occur in spring/summer and those that occur in autumn/winter.

METHODS

The study was conducted in Mato Grosso do Sul, Brazil, from the beginning of the rainy season, November 2020, to October 2021. In a randomized block design, 36 calves were distributed into three groups: (i) negative control; (ii) traditional strategic control in one season (SC1S), at the beginning of the rainy season; and (iii) strategic control in two seasons (SC2S), at the beginning and end of the rainy season. The SC1S strategic control group was treated on day 0, November 2020, and twice more with intervals of 42 days. The SC2S group received three more treatments beginning on day 182, May 2021, with intervals of 42 days. All treatments consisted of 5% fluralaner (Exzolt® 5%) delivered via a pour-on dose of 1 mL/20 kg body weight. Counts of semi-engorged female ticks were performed on day 3 and every 14 days thereafter, and the animals were weighed at the same time.

RESULTS

Fluralaner showed a mean efficacy of more than 95% up to day 294. The two treated groups showed a decrease (P < 0.05) in the average number of ticks on day 3. In the SC2S group, the means were close or equal to zero throughout the study, while in the SC1S group, the means did not differ (P > 0.05) from those of the control group from day 231 onward. The final mean weight gain of each group was 76.40 kg, 98.63 kg, and 115.38 kg for the control, SC1S, and SC2S groups, respectively, differing (P < 0.05) from each other.

CONCLUSIONS

Therefore, three applications of fluralaner, with one application every 42 days from the beginning of the rainy season in the middle spring, resulted in effective tick control for 224 days. When three additional treatments were given in autumn/winter with intervals of 42 days between applications, tick counts were reduced throughout the year. This strategic control approach may be indicated in years with climatic conditions that allow that population peaks are expected to occur in the autumn/winter period.

Resilience of stormwater biofilters following the deposition of wildfire residues: Implication on downstream water quality management in wildfire-prone regions

Stormwater treatment systems such as biofilters could intercept and remove pollutants from contaminated runoff in wildfire-affected areas, ensuring the protection of water quality downstream. However, the deposition of wildfire residues such as ash and black carbon onto biofilters could potentially impair their stormwater treatment functions. Yet, whether and how wildfire residue deposition could affect biofilter functions is unknown. This study examines the impact of wildfire residue deposition on biofilter infiltration and pollutant removal capacities. Exposure to wildfire residues decreased the infiltration capacity based on the amount of wildfire deposited. Wildfire residues accumulated at the top layer of the biofilter, forming a cake layer, but scraping this layer restored the infiltration capacity. While the deposition of wildfire residues slightly changed the pore water geochemistry, it did not significantly alter the removal of metals and E. coli. Although wildfire residues leached some metals into pore water within the simulated root zone, the leached metals were effectively removed by the compost present in the filter media. Collectively, these results indicate that biofilters downstream of wildfire-prone areas could remain resilient or functional and protect downstream water quality if deposited ash is periodically scraped to restore any loss of infiltration capacity following wildfire residue deposition.

Wetness severity increases abrupt shifts in ecosystem functioning in arid savannas

The accelerating pace of climate change has led to unprecedented shifts in surface temperature and precipitation patterns worldwide, with African savannas being among the most vulnerable regions. Understanding the impacts of these extreme changes on ecosystem health, functioning and stability is crucial. This paper focuses on the detection of breakpoints, indicative of shifts in ecosystem functioning, while also determining relevant ecosystem characteristics and climatic drivers that increase susceptibility to these shifts within the semi-arid to arid savanna biome. Utilising a remote sensing change detection approach and rain use efficiency (RaUE) as a proxy for ecosystem functioning, spatial and temporal patterns of breakpoints in the savanna biome were identified. We then employed a novel combination of survival analysis and remote sensing time series analysis to compare ecosystem characteristics and climatic drivers in areas experiencing breakpoints versus areas with stable ecosystem functioning. Key ecosystem factors increasing savanna breakpoint susceptibility were identified, namely higher soil sand content, flatter terrain and a cooler long-term mean temperature during the wet summer season. Moreover, the primary driver of changes in ecosystem functioning in arid savannas, as opposed to wetter tropical savannas, was found to be the increased frequency and severity of rainfall events, rather than drought pressures. This research highlights the importance of incorporating wetness severity metrics alongside drought metrics to comprehensively understand climate-ecosystem interactions leading to abrupt shifts in ecosystem functioning in arid biomes. The findings also emphasise the need to consider the underlying ecosystem characteristics, including soil, topography and vegetation composition, in assessing ecosystem responses to climate change. While this research primarily concentrated on the southern African savanna as a case study, the methodological robustness of this approach enables its application to diverse arid and semi-arid biomes for the assessment of climate-ecosystem interactions that contribute to abrupt shifts.

Possibility of using the STORAGE rainfall generator model in the flood analyses in urban areas

In this investigation, we evaluated the applicability of the Stochastic Rainfall Generator (STORAGE) as a data source for deriving design hydrographs in urban catchments. This assessment involved a comparison with design rainfall calculated using Intensity-Duration-Frequency (IDF) curves derived from observed time-series data. The resulting design rainfall values from both methods were incorporated into a hydrodynamic model of the storm sewer network. To simulate peak discharge and flood areas, the Storm Water Management Model (SWMM) program was employed in conjunction with SCALGO. Our findings indicate that design rainfall values obtained from the STORAGE model exceeded those derived from the observed time-series, with a more pronounced difference for shorter rainfall durations. Simulations further revealed that peak runoff disparities between the two approaches were most evident at a 0.10 probability of exceedance compared to a 0.01 probability. Hydrodynamic simulations demonstrated that the flooding volume induced by design rainfall based on the STORAGE model surpassed that resulting from observed rainfall. Across all events, both the flooding volume and area from STORAGE were consistently greater than those derived from IDF curves. The integration of the SWMM model with the SCALGO application introduced a novel functionality for dynamic visualization of flooding, offering valuable insights for effective flood management in urban areas.

Contribution of rooftop rainwater harvesting to climate adaptation in the city of Hannover: Water quality and health issues of rainwater storage in cisterns and ponds

Rooftop rainwater harvesting systems and blue-green infrastructure are becoming important resilience alternatives for urban climate adaptation. This study sheds light on the largely unreported physicochemical and microbiological quality of private roof-harvested rainwater (RHRW). We aimed to identify the physicochemical and microbiological characteristics of RHRW, explore potential correlations between them and assess probable health risks associated with recreational interactions of children with the water. RHRW was collected from cisterns and ponds located in an inner courtyard in Hanover, Germany. Physicochemical parameters were measured on site and samples were collected once a month in two campaigns in 2020 and 2021. Escherichia coli concentrations ranged from 1 × 10° to 24.1 × 102 MPN/100 mL, Enterococci from 1 × 10° to 19.7 × 102 MPN/100 mL, Salmonella from 1 × 102 to 39 × 103 CFU/100 mL and Pseudomonas aeruginosa from 1 × 10° to 3 × 103 MPN/100 mL. Correlation analysis indicated potential relationships between bacteria, oxygen, and water temperature. The results of the health risk assessment indicated a potential risk of gastrointestinal illnesses due to exposure to Enterococci and Salmonella spp. present in the cisterns and ponds, highlighting the need for appropriate regulations and guidelines for RHRW aimed for non-potable uses. Blue-green infrastructure, when effectively managed and maintained, can offer benefits both by enhancing urban climate resilience and promoting citizens well-being.

A comparative analysis of methods and tools for low impact development (LID) site selection

The site selection for Low Impact Development (LID) practices is a significant process. It affects the effectiveness of LID in controlling stormwater surface runoff, volume, flow rate, and infiltration. This research paper presents a comprehensive review of various methods used for LID site selection. It starts by introducing different methods and tools. Three main methods: index-based methods, GIS-based multi-criteria decision analysis (MCDA), and multi-criteria models and tools, are discussed in detail. A comparative analysis of these methods is then conducted based on ten different criteria. These criteria include the number of variables, data properties, the scale of analysis, benefits maximization approach, multi-attribute decision analysis, user-friendliness, community and stakeholder participation, and the validation methods. This comparison reveals limitations in each method. These include inadequate data availability and quality, lack of evaluation methods, comprehensive assessment criteria and spatial explicitness. These challenges underscore the need for future research to prioritize spatial clarity, broaden criteria, improve data quality through standardization, incorporate field visits and remote sensing for robust results, integrate big data, and develop web-based, open-source tools for enhanced accessibility. These key strategies provide valuable insights for advancing LID site selection methods.

Coupling machine learning and physical modelling for predicting runoff at catchment scale

In this paper, we present an approach that combines data-driven and physical modelling for predicting the runoff occurrence and volume at catchment scale. With that aim, we first estimated the runoff volume from recorded storms aided by the Green-Ampt infiltration model. Then, we used machine learning algorithms, namely LightGBM (LGBM) and Deep Neural Network (DNN), to predict the outputs of the physical model fed on a set of atmospheric variables (relative humidity, temperature, atmospheric pressure, and wind velocity) collected before or immediately after the beginning of the storm. Results for a small urban catchment in Madrid show DNN performed better in predicting the runoff occurrence and volume. Moreover, enriching the input primary atmospheric variables with auxiliary variables (e.g., storm intensity data recorded during the first hour, or rain volume and intensity estimates obtained from auxiliary regression methods) largely increased the model performance. We show in this manuscript data-driven algorithms shaped by physical criteria can be successfully generated by allowing the data-driven algorithm learn from the output of physical models. It represents a novel approach for physics-informed data-driven algorithms shifting from common practices in hydrological modelling through machine learning.

Tool for fast assessment of stormwater flood volumes for urban catchment: A machine learning approach

Specific flood volume is an important criterion for evaluating the performance of sewer networks. Currently, mechanistic models - MCMs (e.g., SWMM) are usually used for its prediction, but they require the collection of detailed information about the characteristics of the catchment and sewer network, which can be difficult to obtain, and the process of model calibration is a complex task. This paper presents a methodology for developing simulators to predict specific flood volume using machine learning methods (DNN - Deep Neural Network, GAM - Generalized Additive Model). The results of Sobol index calculations using the GSA method were used to select the ML model as an alternative to the MCM model. It was shown that the DNN model can be used for flood prediction, for which high agreement was obtained between the results of GSA calculations for rainfall data, catchment and sewer network characteristics, and calibrated SWMM parameters describing land use and sewer retention. Regression relationships (polynomials and exponential functions) were determined between Sobol indices (retention depth of impervious area, correction factor of impervious area, Manning's roughness coefficient of sewers) and sewer network characteristics (unit density of sewers, retention factor - the downstream and upstream of retention ratio) obtaining R2 = 0. 55-0.78. The feasibility of predicting sewer network flooding and modernization with the DNN model using a limited range of input data compared to the SWMM was shown. The developed model can be applied to the management of urban catchments with limited access to data and at the stage of urban planning.

Profiling emerging micropollutants in urban stormwater runoff using suspect and non-target screening via high-resolution mass spectrometry

Urban surface runoff contains chemicals that can negatively affect water quality. Urban runoff studies have determined the transport dynamics of many legacy pollutants. However, less attention has been paid to determining the first-flush effects (FFE) of emerging micropollutants using suspect and non-target screening (SNTS). Therefore, this study employed suspect and non-target analyses using liquid chromatography-high resolution mass spectrometry to detect emerging pollutants in urban receiving waters during stormwater events. Time-interval sampling was used to determine occurrence trends during stormwater events. Suspect screening tentatively identified 65 substances, then, their occurrence trend was grouped using correlation analysis. Non-target peaks were prioritized through hierarchical cluster analysis, focusing on the first flush-concentrated peaks. This approach revealed 38 substances using in silico identification. Simultaneously, substances identified through homologous series observation were evaluated for their observed trends in individual events using network analysis. The results of SNTS were normalized through internal standards to assess the FFE, and the most of tentatively identified substances showed observed FFE. Our findings suggested that diverse pollutants that could not be covered by target screening alone entered urban water through stormwater runoff during the first flush. This study showcases the applicability of the SNTS in evaluating the FFE of urban pollutants, offering insights for first-flush stormwater monitoring and management.

Effects of urban catchment characteristics on combined sewer overflows

Pollution from Combined Sewer Overflows (CSOs) cause diffuse environmental problems, which are still not satisfactorily addressed by current management practices. In this study, a sensitivity analysis was conducted on several CSO environmental impact indicators, with respect to parameters that characterise climate, urban catchment and the CSO structure activation threshold. The sensitivity analysis was conducted by running 10000 simulations with the Storm Water Management Model, using a simplified modelling approach. The indicators were calculated at yearly scale to evaluate overall potential effects on water bodies. The results could be used to estimate pollution load ranges, known the values of the input parameters, and to investigate suitable strategies to reduce pollution of the receiving water bodies. The percentage of impervious surface of the catchment was found the most influent parameter on all the indicators, and its reduction can contain the discharged pollutant mass. The activation threshold, instead, resulted the second least influent parameter on all the indicators, suggesting that its regulation alone would not be a suitable strategy to reduce CSO pollution. However, along with the reduction of the imperviousness, its increase could effectively decrease the concentration of pollutant in the overflow. The results also indicate that neither adopting sustainable urban drainage practices, nor interventions on the CSO device, significantly affect the frequency of the overflows. Therefore, restricting this latter was found to be ineffective for the reduction of both the discharged pollutant mass and the concentration of pollutant in the overflow.

The investigation of sorption-desorption performance and mechanism of copper by surfactant-modified zeolite in aqueous solutions

As the most common filler in stormwater treatment, zeolite (NZ-Y) has good cation exchange capability and stabilization potential for the removal of heavy metal from aqueous solutions. In this study, sodium dodecyl sulfate (SDS) and NZ-Y were selected to preparing new adsorbent (SDS-NZ) by using a simple hydrothermal method. The sorption-desorption performance and mechanism of Cu(II) onto SDS-NZ were investigated. The results showed that the sorption of Cu(II) on SDS-NZ was in accordance with the pseudo-second-order kinetic model with an equilibrium time of 4 h. The sorption behavior fitted Langmuir isotherm with a saturation sorption capability of 9.03 mg/g, which was three times higher than that of NZ-Y. The modification of SDS increases the average pore size of NZ-Y by 3.96 nm, which results in a richer internal pore structure and more useful sorption sites for Cu(II) sorption. There was a positive correlation between solution pH values and sorption capability of Cu(II) in the range of 3.0-6.0. With the ionic strength increased, the sorption capability of Cu(II) onto SDS-NZ first decreased and then increased, which may be attributed to competitive sorption and compression of the electronic layer. The desorption of Cu(II) on SDS-NZ was favored by the increase in SDS concentration and ionic strength and decrease in solution pH values. The application of SDS-NZ in runoff improved the leaching risk of Cu(II). After several cycles, the ability of reused SDS-NZ to efficiently adsorb most heavy metals was verified with removal rates above 99%.

Physiological and biochemical assortment in different wheat genotypes (Triticum aestivum L.) under rain fed conditions

Wheat (Triticum aestivum L.) is the most extensively cultivated cereal crop in the world; however, its growth and development are affected by different types of biotic and abiotic stress conditions. The aim of this study was to assess the physico-chemical diversity in different wheat genotypes under rain-fed conditions. Principle component analysis (PCA) showed that significant variation for different components contributed 77.87% of total variability among all genotypes. In the scree plot, the first two PCs (PC1=44.75%, PC2=14.28%) had significant differences for numerous agronomic traits. The scatter biplot depicted eight genotypes (Zardana, NR-462, D-97, BARS-2009 (a check), NR-481, Tarnab-73, NR-489 and Pirsabak-91) with high diversity (variation ~90%) for different morphological traits, identifiable as they were located further away from the origin than other genotypes. Factor analysis of loading factors among wheat genotypes across different morpho-physiological traits also showed significant diversity for positive and negative loads. In cluster analysis, genotypes such as BWP-97, BARS-2009, NR-489, NR-448 and Pak. 13 were outliers, indicating significant diversity among all genotypes for different agronomic traits. Biochemical analysis showed maximum values for antioxidant activity, total phenolic content, and total flavonoid content in lines NR-485 (93.76%), NR-489 (3.55mg gallic acid equivalent (GAE)/g), and the variety Suleman-96 (3.45mg quercetin equivalent (QE)/g), respectively. This study provides new insights for understanding the diversity of different wheat genotypes under rain-fed conditions, and the selected genotypes can be evaluated for further breeding programs.

Analysis of pollutant dispersion patterns in rivers under different rainfall based on an integrated water-land model

In the context of rapid urban expansion, the interaction between humanity and nature has become more prominent. Urban land and rivers often exist as distinct entities with limited material exchange. However, during rainfall, these two systems interconnect, resulting in the transfer of land-derived pollutants into rivers. Such transfer significantly increases river pollutant levels, adversely affecting water quality. Therefore, developing a water quality simulation and prediction model is crucial. This model should effectively illustrate pollutant movement and dispersion during rain events. This study proposes a comprehensive model that merges the Storm Water Management Model (SWMM) with the Environmental Fluid Dynamics Code (EFDC). This integrated model assesses the spread and dispersion of pollutants, including Ammonia Nitrogen (NH3-N), Total Phosphorus (TP), Total Nitrogen (TN), and Chemical Oxygen Demand (COD), within urban water cycles for various rainfall conditions, thus offering critical theoretical support for managing the water environment. The application of this model under different rainfall intensities (light, moderate and heavy) provides vital insights. During light rainfall, the river's natural purification process can sustain surface water quality at Class IV. Moderate rainfall causes accumulation of pollutants, reducing water quality to Class V. Conversely, heavy rainfall rapidly increases pollutant concentrations due to higher inflow, pushing the river to a degraded Class V status, which is beyond its natural purification capacity, necessitating engineering solutions to reattain Class IV quality. Furthermore, pollutant accumulation in downstream river sections is more influenced by flow rate than by rainfall intensity. In summary, the SWMM-EFDC integrated model proves highly effective in predicting river water quality, thereby significantly aiding urban water pollution control.

Profiling wastewater characteristics in intra-urban catchments using online monitoring stations

This study aims to investigate the differences in intra-urban catchments with different characteristics through real-time wastewater monitoring. Monitoring stations were installed in three neighbourhoods of Barcelona to measure flow, total chemical oxygen demand (COD), pH, conductivity, temperature, and bisulfide (HS-) for 1 year. Typical wastewater profiles were obtained for weekdays, weekends, and holidays in the summer and winter seasons. The results reveal differences in waking up times and evening routines, commuting behaviour during weekends and holidays, and water consumption. The pollutant profiles contribute to a better understanding of pollution generation in households and catchment activities. Flows and COD correlate well at all stations, but there are differences in conductivity and HS- at the station level. The article concludes by discussing the operational experience of the monitoring stations.

New phosphorus losses via tile drainage depend on fertilizer form, placement, and timing

Agricultural phosphorus (P) losses are harmful to water quality, but knowledge gaps about the importance of fertilizer management practices on new (recently applied) sources of P may limit P loss mitigation efforts. Weighted regression models applied to subsurface tile drainage water quality data enabled estimating the new P losses associated with 155 P applications in Ohio and Indiana, USA. Daily discharge and dissolved reactive P (DRP) and total P (TP) loads were used to detect increases in P loss following each application which was considered new P. The magnitude of new P losses was small relative to fertilizer application rates, averaging 79.3 g DRP ha-1 and 96.1 g TP ha-1 , or <3% of P applied. The eight largest new P losses surpassed 330 g DRP ha-1 or 575 g TP ha-1 . New P loss mitigation strategies should focus on broadcast liquid manure applications; on average, manure applications caused greater new P losses than inorganic fertilizers, and surface broadcast applications were associated with greater new P losses than injected or incorporated applications. Late fall applications risked having large new P losses applications. On an annual basis, new P contributed an average of 14% of DRP and 5% of TP losses from tile drains, which is much less than previous studies that included surface runoff, suggesting that tile drainage is relatively buffered with regard to new P losses. Therefore old (preexisting soil P) P sources dominated tile drain P losses, and P loss reduction efforts will need to address this source.

A critical review of the mechanisms, factors, and performance of pervious concrete to remove contaminants from stormwater runoff

Stormwater can carry pollutants accumulated on impervious surfaces in urban areas into natural water bodies in absence of stormwater quality improvement devices. Pervious concrete (PC) pavement is one of the low-impact development practices introduced for urban flooding prevention and stormwater pollution reduction. PC removes various types of water contaminants. Mechanisms contributing to the water pollution removal capacity of PC can be categorized into three groups: physical, chemical, and biological. Properties of PC such as permeability, porosity, thickness, and adsorption capacity influence removal of all contaminants, although their impact might differ depending on the pollutant properties. Chemical mechanisms include precipitation, co-precipitation, ion and ligand exchange, complexation, diffusion, and sorption. Bulk organics and nutrients are removed primarily by biodegradation. Physical filtration is the primary mechanism to retain suspended solids, although biological activities may have a minor contribution. Release of calcium (Ca2+) and hydroxide (OH-) from hardened cement elevates the effluent pH, which is an environmental concern. However, the pH elevation is also the prime contributor to heavy metals and nutrients removal through precipitation. Specific cementitious materials (e.g., Pozzolans and nanoparticles) and carbonation curing approach are recommended to control effluent pH elevation. Complexation, diffusion, ion solubility, and stability constants are other mechanisms and parameters that influence heavy metal removal. Organic matter availability, electrostatic attraction, temperature, pH, contact time, specific surface area, and roughness of PC pores contribute to the pathogen removal process. Although PC has been found promising in removing various water pollutants, limited salinity removal can be achieved due to the inherent release of Ca2+and OH- from PC.

The effect of green infrastructure on resilience performance in combined sewer systems under climate change

Climate change is currently reshaping precipitation patterns, intensifying extremes, and altering runoff dynamics. Particularly susceptible to these impacts are combined sewer systems (CSS), which convey both stormwater and wastewater and can lead to combined sewer overflow (CSO) discharges during heavy rainfall. Green infrastructure (GI) can help mitigate these discharges and enhance system resilience under historical conditions; however, the quantification of its effect on resilience in a future climate remains unknown in the literature. This study employs a modified Global Resilience Analysis (GRA) framework for continuous simulation to quantify the impact of climate change on CSS resilience, particularly CSOs. The study assesses the efficacy of GI interventions (green roofs, permeable pavements, and bioretention cells) under diverse future rainfall scenarios based on EURO-CORDEX regional climate models (2085-2099) and three Representative Concentration Pathways (2.6, 4.5, 8.5 W/m2). The findings underscore a general decline in resilience indices across the future rainfall scenarios considered. Notably, the total yearly CSO discharge volume increases by a range of 145 % to 256 % in response to different rainfall scenarios. While GI proves effective in increasing resilience, it falls short of offsetting the impacts of climate change. Among the GI options assessed, green roofs routed to pervious areas exhibit the highest adaptive capacity, ranging from 9 % to 22 % at a system level, followed by permeable pavements with an adaptation capacity between 7 and 13 %. By linking the effects of future rainfall scenarios on CSO performance, this study contributes to understanding GI's potential as a strategic tool for enhancing urban resilience.

Microbial composition of Saharan dust plumes deposited as red rain in Granada (Southern Spain)

During durst storms, also biological material is transported from arid areas such as the Sahara Desert. In the present work, rain samples containing significant amounts of mineral dust have been collected in Granada during different red rain episodes. Biological features (bacteria, biofilm, pollen grain and fungal spore) as well as size-particle distribution and mineralogical composition were studied by SEM. Nanobacteria were observed for the first time in red rain samples. A preliminary metabarcoding analysis was performed on three red rain samples. Here, Bacillota made up 18 % and Pseudomonadota 23 % of the whole prokaryotic community. The fungal community was characterized by a high abundance of Ascomycota and, dependent on the origin, the presence of Chytridiomycota. By means of 16S rRNA sequencing, 18 cultivable microorganisms were identified. In general, members of the phyla Pseudomonadota and Bacillota made up the majority of taxa. Some species, such as Peribacillus frigoritolerans and Bacillus halotolerans were isolated during three different red rain episodes. Generally, red rain carries a wide variety of microorganisms, being their ecosystem and health effects largely unknown.

The effects of heavy rain on the fate of urban and agricultural pollutants in the riverside area around weirs using multi-isotope, microbial data and numerical simulation

The increase in extreme heavy rain due to climate change is a critical factor in the fate of urban and agricultural pollutants in aquatic system. Nutrients, including NO3- and PO43-, are transported with surface and seepage waters into rivers, lakes and aquifers and can eventually lead to algal blooms. δ15N-NO3-, δ18O-NO3-, and δ11B combined with hydrogeochemical and microbial data for groundwater and surface water samples were interpreted to evaluate the fate of nutrients in a riverside area around weirs in Daegu, South Korea. Most of the ions showed similar concentrations in the groundwater samples before and after heavy rain while concentrations of major ions in surface water samples were diluted after heavy rain. However, Si, PO43-, Zn, Ce, La, Pb, Cu and a number of waterborne pathogens increased in surface water after heavy rain. The interpretation of δ11B, δ15N-NO3-, and δ18O-NO3- values using a Bayesian mixing model revealed that sewage and synthetic fertilizers were the main sources of contaminants in the groundwater and surface water samples. δ18O and SiO2 interpreted using the Bayesian mixing model indicated that the groundwater component in the surface water increased from 4.4 % to 17.9 % during the wet season. This is consistent with numerical simulation results indicating that the direct surface runoff and the groundwater baseflow contributions to the river system had also increased 6.4 times during the wet season. The increase in proteobacteria and decrease of actinobacteria in the surface water samples after heavy rain were also consistent with an increase of surface runoff and an increased groundwater component in the surface water. This study suggests that source apportionment based on chemical and multi-isotope data combined with numerical modeling approaches can be useful for identifying main hydrological and geochemical processes in riverside areas around weirs and can inform suggestions of effective methods for water quality management.

Changes in vegetation in China's drylands are closely related to afforestation compared with climate change

The response of vegetation to climate change and human activities has attracted considerable attention. However, quantitative studies on the effects of climate change and human activities on dryland vegetation in different seasons remain unclear. This study investigated the impacts of precipitation, temperature, soil water storage (SWS) (top [0-7 cm], shallow [7-28 cm], and middle [28-100 cm] layers), vapor pressure deficit (VPD), and afforestation on vegetation as well as their relative contribution rates during the rainy season ([RS], June to September), dry season ([DS], November to April), transition season ([TS], May and October), and all year period (AY) in China's drylands from 2001 to 2020 using the first-difference method. Areas with precipitation and SWS showing significant positive correlation with dryland vegetation (p < 0.05) were found to be larger in RS than in DS and TS, and the positive effect of SWS increased with soil depth in the 0-28 cm interval. Increasing VPD induced a significant negative effect on vgetation during RS but it was not predominant in DS and TS. Afforestation showed an extremely significant positive correlated with dryland vegetation across >60 % of China's dryland areas (p < 0.01), but this improvement was found to be limited to regions with the highest afforestation area. Moreover, dryland vegetation dynamics were driven by afforestation in all seasons, with contribution rates of 64.23 %-71.46 %. The effects of SWS and VPD on vegetation driven by precipitation and temperature exceeded the direct effects of precipitation and temperature. Among climatic factors, VPD showed a major regulating effect on dryland vegetation at the top and shallow soil layers in almost all seasons, whereas the relative contribution rate of SWS increased with soil layer. The findings can provide a scientific reference for the sustainable development and protection of drylands under global warming.

Dancing in the purple rain: color affinity and oviposition choices in Aedes sierrensis (Diptera: Culicidae)

The western tree hole mosquito, Aedes sierrensis (Ludlow) (Diptera: Clucidae), is a pestiferous mosquito with a range extending over the entire pacific seaboard and into portions of the intermountain west. As a peridomestic heartworm vector, it demands at least some level of surveillance to understand its abundance. However, the species is refractory to a majority of conventional vector surveillance approaches for tracking mosquitoes. To find more options for Aedes sierrensis surveillance, a variety of oviposition attractants were evaluated in arena-style choice assays using colony reared adults. A range of infusion treatments (e.g., alfalfa, oak, and beetroot) were examined and then combined with investigations of liquid color as well as ovicup color and entryway position. These studies revealed that Ae. sierrensis have an affinity for purple coloration, plain water, and larger entryway sizes for oviposition cups. A prototype ovicup was 3D-printed using purple filament and multiple types of entryways, and used to re-test infusion waters. No particular attraction differences were detected after normalizing for purple color. Comparisons to black 3D-printed cups yielded surprising observations that male mosquitoes also aggregated on purple cups while females sheltered, but not necessarily oviposited, in black cups. Although this was only a laboratory-based assessment, these studies provide useful information for future field trials of potential oviposition traps for surveillance of Ae. sierrensis.