Assessing the utility of shellfish sanitation monitoring data for long-term estuarine water quality analysis.

Regular testing of coastal waters for fecal coliform bacteria by shellfish sanitation programs could provide data to fill large gaps in existing coastal water quality monitoring, but research is needed to understand the opportunities and limitations of using these data for inference of long-term trends. In this study, we analyzed spatiotemporal trends from multidecadal fecal coliform concentration observations collected by a shellfish sanitation program, and assessed the feasibility of using these monitoring data to infer long-term water quality dynamics. We evaluated trends in fecal coliform concentrations for a 20-year period (1999-2021) using data collected from spatially fixed sampling sites (n = 466) in North Carolina (USA). Findings indicated that shellfish sanitation data can be used for long-term water quality inference under relatively stationary management conditions, and that salinity trends can be used to investigate management-driven bias in fecal coliform observations collected in a particular area.

Geo-statistical analysis of climatic variability and trend detection in the Hindu Kush region, North Pakistan.

This paper is an effort of geo-statistical analysis of rainfall variability and trend detection in the eastern Hindu Kush region located in the north-west of Pakistan. The eastern section of the HK region lies in the western part of Pakistan. Exploring rainfall variability and quantifying its trend and magnitude is one of the key indicators among all climatic parameters. In the study area, Pakistan Meteorology Department (PMD) has established seven meteorological stations: Drosh, Chitral, Dir, Timergara, Saidu Sharif, Malam Jabba, and Kalam. Daily, mean monthly, and mean annual rainfall time series data for all the met stations were geo-statistically analyzed in the GIS environment for detecting monthly and annual variability in rainfall, variability, and trend detection. Mann-Kendall (MK) and Theil-Sen's slope (TSS) statistical tests were applied to rainfall data. Initially, the MK test was applied for detection of trends and TSS test was used to quantify the change in magnitude. The results indicate that the rainfall variability in intensity and trend pattern detection. The analysis confirms that an extremely significant rainfall trend in the case of mean annual rainfall was predicted at Dir and Malam Jabba meteorological stations. Opposite to this, at Kalam and Chitral stations, a less significant rainfall trend was noted. In a similar context, no prominent rainfall trend has been found at Drosh, Timergara, and Saidu Sharif meteorological stations. Likewise, using TSS, an extremely negative variation in the magnitude of rainfall was verified at Kalam and Malam Jabba. However, a noteworthy positive change in rainfall magnitude has been noted at Dir and Saidu Sharif meteorological stations. The findings of this research have the potential to assist the decision and policy makers and academicians to think truly and conduct more scientific research studies to mitigate climate change.

Quantitative assessment of the variations in monthly precipitation trends induced by the impact of three gorges dam.

There are many studies that have examined the impact of the Three Gorges Dam (TGD) on changes in meteorological data, and most of them concluded that the TGD significantly reduced precipitation without taking into account the negative trends that had already existed before the impoundment. In this study, the investigation focused on the monthly precipitation data, and the Mann-Kendall (MK) trend analysis was conducted to show that the TGD had little effect on the trends of the precipitation data. Monthly data (1980-2018) from 19 stations upstream and downstream of the TGD and 5 stations located far from the main river were extracted. The analysis and results showed that although the linear long-term (1980-2018) precipitation trend upstream of the TGD was downward, the MK trend analysis showed that the precipitation trends became upward after impoundment. This situation existed even for station data located outside the region. Also, the analysis of monthly trends in different seasons showed that in spring and winter, there was only a very weak downward trend in monthly precipitation, while in summer and autumn, the trends were upward with steeper slopes. Following the upward trends of the monthly precipitation, the TGD generally positively intensified the monthly precipitation trends upstream and downstream of the dam, with the exception of a few months when total precipitation amounts were consistently low. In contrast to the trend analysis, which showed small and insignificant variations in precipitation data, the 12-month SPEI analysis showed a significant deterioration (about 20%) in the wetness index after impoundment both upstream and downstream of the TGD, while this situation did not occur outside the region. Thus, the TGD has extended dry periods both upstream and downstream of the dam over the past two decades.

Analysis of aerosol optical depth and relation to covariates during pre-monsoon season (2002-2019) over Pakistan using ARIMAX model and cross-wavelet analysis.

The pre-monsoon season heavily influences the precipitation amount in Pakistan. When hydrometeorological parameters interact with aerosols from multiple sources, a radiative climatic response is observed. In this study, aerosol optical depth (AOD) space-time dynamics were analyzed in relation to meteorological factors and surface parameters during the pre-monsoon season in the years 2002-2019 over Pakistan. Level-3 (L3) monthly datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-Angle Imaging Spectroradiometer (MISR) were used. Tropical Rainfall Measuring Mission (TRMM) derived monthly precipitation, Atmospheric Infrared Sounder (AIRS) derived air temperature, after moist relative humidity (RH) from Modern-Era Retrospective analysis for Research and Applications, Version-2 (MERRA-2), near-surface wind speed, and soil moisture data derived from Global Land Data Assimilation System (GLDAS) were also used on a monthly time scale. For AOD trend analysis, Mann-Kendall (MK) trend test was applied. Moreover, Autoregressive Integrated Moving Average with Explanatory variable (ARIMAX) technique was applied to observe the actual and predicted AOD trend, as well as test the multicollinearity of AOD with covariates. The periodicities of AOD were analyzed using continuous wavelet transformation (CWT) and the cross relationships of AOD with prevailing covariates on a time-frequency scale were analyzed by wavelet coherence analysis. A high variation of aerosols was observed in the spatiotemporal domain. The MK test showed a decreasing trend in AOD which was most significant in Baluchistan and Punjab, and the overall trend differs between MODIS and MISR datasets. ARIMAX model shows the correlation of AOD with varying meteorological and soil parameters. Wavelet analysis provides the abundance of periodicities in the 2-8 months periodic cycles. The coherency nature of the AOD time series along with other covariates manifests leading and lagging effects in the periodicities. Through this, a notable difference was concluded in space-time patterns between MODIS and MISR datasets. These findings may prove useful for short-term and long-term studies including oscillating features of AOD and covariates.

Species composition and spatial and temporal distribution of occasional fishes in Haizhou Bay, Shandong, China.

Occasional species are often neglected in community ecology studies due to low abundance and frequence. However, they are closely related to biodiversity maintenance and marine conservation. We used index of relative importance (IRI), distribution center of gravity and Mann-Kendall trend test to elucidate the spatial and temporal distributions and changing trends of occasional fishes in Haizhou Bay based on the fishery resources survey data collected from 2013 to 2019. The occasional fishes in Haizhou Bay was identified based on IRI. Nine of the occasional species were selected to analyze the spatial distribution and interannual trends of relative biomass and occurrence frequency. The results showed that a total of 93 fish species were recorded in the multi-year survey in Haizhou Bay. Among which, 61 species had IRI<10, indicating the existence of a large number of occasional fishes in Haizhou Bay. The nine selected occasional species had distinctive spatial distribution characteristics. Erisphex pottii, Azuma emmnion, and Raja porosa were mainly distributed in the eastern part of Haizhou Bay, with a wide habitat range. Sphyraena pinguis, Coilia nasus, and Zebrias zebra were distributed in the western and southern parts, respectively. The rest species were mainly distributed in the southwestern part. In terms of interannual variation, the biomass and occurrence frequency of Coilia mystus, R. porosa, A. emmnion, and S. pinguis showed fluctuating changes, while Odontamblyopus rubicundus and Ctenotrypauchen microcephalus showed an increasing trend. The biomass and occurrence of Z. zebra, C. nasus and E. pottii gradually decreased with no catch in recent years. This study would contribute to the understanding of the spatial and temporal distribution of occasional species, and provide guidance for the conservation of occasional biological resources and species diversity, especially for the establishment of marine protected areas.

National trends in distribution of underrepresented minorities within United States general surgery residency programs: A longitudinal panel study.

BACKGROUND: Cultural affinity with a provider improves satisfactoriness of healthcare. We examined 2005-2019 trends in racial/ethnic diversity/inclusion within general surgery residency programs. METHODS: We triangulated 2005-2019 race/ethnicity data from Association of American Medical Colleges surveys of 4th-year medical students, the Electronic Residency Application Service, and Accreditation Council for Graduate Medical Education-affiliated general surgery residencies. Temporal trends in minority representation were tested for significance. RESULTS: Underrepresented racial/ethnic minorities in medicine (URiMs) increased among graduating MDs from 7.6% in 2005 to 11.8% in 2019 (p < 0.0001), as did their proportion among surgery residency applicants during 2005-2019 (p < 0.0001). However, proportions of URiMs among general surgery residents (≈8.5%), and of programs without URiMs (≈18.8%), stagnated. CONCLUSIONS: Growing URiM proportions among medical school graduates and surgery residency applicants did not improve URiM representation among surgery trainees nor shrink the percentage of programs without URiMs. Deeper research into motivators underlying URiMs' residency program preferences is warranted.

The detection and monitoring of pollution caused by gold mining using a vegetation cover index.

This study explores how a vegetation cover (VC) index can be employed as a pollution warning tool in gold mining areas in the Northwest of Iran. The analysis included the following: (a) the extraction of normalized difference vegetation index (NDVI) maps from Landsat images in three zones, i.e., mining operations, upstream areas without any exploration, and the downstream area of the mining activities, (b) calculation of the zones' VC, (c) investigation of transformation trends in each pixel of VC time series using the Mann-Kendall trend test, (d) determination of the pixels with significant VC reduction and the significant starting points of the trend using the sequential Mann-Kendall test, (e) assessment of the correlation between the zones with significantly reduced VC, and (f) a correlation test between average monthly and annual climate parameters and VC. Our results indicate that although 51 ha of VC has been demolished around the mining activities areas (i.e., zone 1), an overall upward trend in vegetation with no chemical leakage is observed into the downstream area of the basin (i.e., zone 3). This upward trend can be mostly attributed to the increasing precipitation and decreasing temperature in the study period. The fact that the area downstream of the mine shows that the heap leaching method for gold mining in Andaryan mine is currently not damaging the vegetation, this likely means that there is no leakage to the surrounding environment from the mine. Our results further show that using NDVI in a pixel-based scale and statistical methods has a high potential to quantify the effects of human activities on surface biophysical characteristics.

Hotspot and trend analysis of forest fires and its relation to climatic factors in the western Himalayas.

Forest fire is one of the main issues of forest ecosystems around the world which has resulted in loss of biodiversity, forest degradation, soil erosion, and greenhouse gas emission. Ironically, the information on the forest fire regime and its pattern are still lacking in the Himalayan region. In this study, Moderate-Resolution Imaging Spectroradiometer active fire data products from 2001 to 2020 have been analysed for understanding the forest fire trends and its hotspots patterns during the active fire season (February to June). About 1347 average fire counts/year were recorded in six natural vegetations with the highest number of fires observed during the year 2012 (n = 3096) and minimum in 2011 (n = 210). Mann-Kendall trends analysis for the spatial and temporal pattern of fires indicated that there is a significant increase of forest fires towards higher elevation. Forest fire hotspot analysis using fire radiative power, fire frequency, and fire density showed that Uttarakhand is the most forest fire-prone state as compared to other north-western Himalayan states. It is also revealed that the May month has a higher number of fire counts and the evergreen needle forests have higher fire frequencies amongst the forest types. The forest fires were found to be more influenced by land surface temperature as compared to rainfall. The outcomes in this study on the temporal and spatial patterns for forest fire can be used for forest fire modelling. Supplementary Information: The online version contains supplementary material available at 10.1007/s11069-022-05530-5.

Surface urban heat island and its relationship with land cover change in five urban agglomerations in China based on GEE.

The development of urbanization has changed the original land cover and exacerbated the urban heat island effect, seriously affecting the sustainable development of the ecological environment. Research on urban heat island characteristics and land cover changes in five major urban agglomerations in China to provide a reference for preventing thermal environmental risks and urban agglomeration construction planning. This paper estimates the surface urban heat island intensity (SUHII) of the five major urban agglomerations in China from 2003 to 2019 based on Google Earth Engine (GEE) through the urban-rural dichotomy, analyzes their trends through the Sen + M-K trend analysis method, and combines the detrending rate matrix to analyze the impact of land cover type shift on urban heat island change. Research shows that (1) the land cover types of the five major urban agglomerations in China have changed considerably from 2003 to 2019, and all five major urban agglomerations in China experienced varying degrees of urban expansion. (2) The annual average value of SUHII decreases in Beijing-Tianjin-Hebei, Yangtze River Delta, and middle reaches of the urban agglomerations, while the annual average value of SUHII increases in Chengdu-Chongqing and Pearl River Delta urban agglomerations. (3) The spatial composition of land cover types in the five major urban agglomerations in China is highly spatially correlated with urban heat islands, with urban land and bare land urban heat islands being the most pronounced. (4) The land cover type shift has the most significant heat island impact on Beijing-Tianjin-Hebei, Yangtze River Delta, and Chengdu-Chongqing urban agglomerations. (5) The land cover change (LCC) with an increasing trend in SUHII is mainly bare land converted to arable land, and water bodies, grassland, forest land, and arable land converted to urban land.

Identification of precipitation trend and landslide susceptibility analysis in Miandoab County using MATLAB.

Iranian plateau is a seismically active region. Within this region, northwestern Iran is of high importance. Before proper planning for mitigating the earthquake-induced hazards can be achieved, it is necessary to identify high-risk areas in terms of susceptibility to earthquakes. In this study, landslide susceptibility in Miandoab Country was modeled using the so-called random forest algorithm (RFA) in MATLAB based on records acquired at 67 earthquake hotspots considering 9 factors affecting the earthquake occurrence (i.e., height, slope, direction, distance from fault, distance from river, distance from road, land use, geology, and precipitation). Predictive power of the model and validity of its results were evaluated using relative operating characteristic (ROC) curve and area under the curve (AUC). The assessment results showed very good accuracy of the model (0.97). It was further found that digital height layer, geology, and distance from fault impose the largest contributions into earthquake potential. The results also showed that 53%, 8.3%, and 38.4% of the study area were classified as being at low risk, moderate risk, and high risk of earthquake. Among other climatic elements, the precipitation exhibits the largest fluctuations; we proceeded to evaluate precipitation trends in the study area for a statistical period of 30 years. This was practiced by implementing Mann-Kendall nonparametric test in MATLAB. This subject-matter is especially important in Iran where the annual precipitation level is as low as 250 mm. The results showed that the precipitation follows an increasing trend in the region.

Impact of 2nd wave of COVID-19-related lockdown on coastal water quality at Diu, western coast of India and role of total alkalinity on bacterial loads.

A detailed coastal water monitoring near Diu coast, western part of India was performed from October, 2020 to May, 2021 covering the 2nd lockdown time. Average monthly fluctuation from 7 different sampling stations of total 9 physico-chemical parameters such as pH, salinity, turbidity, nitrite (NO2), nitrate (NO3), ammonia (NH3), phosphate (PO4), total alkalinity and silicate were recorded. Initially, Mann-Kendall trend test for all the 9 parameters showed non-zero trend, which may be either linear or non-linear. During 2nd lockdown period, there was a fluctuation of value for parameters like pH, salinity, nitrate, nitrite and phosphate. Average total bacterial count and differential bacterial count also gradually decreased from March, 2021 sampling. Principal component analysis (PCA) plot covering all the physico-chemical parameters as well as the differential bacterial count showed a distinct cluster of all bacterial count with total alkalinity value. Subsequently, mathematical equation was formulated between total alkalinity value and all differential bacterial count. Upto our knowledge, this is the first report where mathematical equation was formulated to obtain value of different bacterial load based on the derived total alkalinity value of the coastal water samples near Diu, India.

Spatiotemporal variability of land surface temperature in north-western Ethiopia.

The aggravating deforestation, industrialization, and urbanization are becoming the principal causes for environmental challenges worldwide. As a result, satellite-based remote sensing helps to explore the environmental challenges spatially and temporally. This investigation analyzed the spatiotemporal variability in land surface temperature (LST) and its link with elevation in the Amhara region, Ethiopia. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST data (2001-2020) were used. The pixel-based linear regression model was used to explore the spatiotemporal variability of LST changes. Furthermore, Sen's slope and Mann-Kendall trend test were used to determine the magnitude of temporal shifts of the areal average LST and evaluate trends in areal average LST, respectively. Coefficient of variation (CV) was also used to analyze spatial and temporal variability in seasonal and annual LST. The seasonal LST CV varied from 1.096-10.72%, 0.7-11.06%, 1.29-14.76%, and 2.19-10.35% for average autumn (September to November), summer (June to August), spring (March to May), and winter (December to February) seasons, respectively. The highest inter-annual variability was observed in the eastern, northern, and south-western districts than that in the other parts. The seasonal spatial LST trend varied from -0.7-0.16, -0.4-0.224, 0.6-0.19, and -0.6-0.32 for average autumn, summer, spring, and winter seasons, respectively. Besides, the annual spatial LST slope varied from -0.58 to 0.17. Negative slopes were found in the central, mid-western, and mid-northern districts in annual LST, unlike the other parts. The annual variations of mean areal LST decreased insignificantly at the rate of 0.046°C year-1 (P<0.05). However, the inter-annual variability trend of annual LST increased significantly. Generally, the LST is tremendously variable in space and time and negatively correlated with elevation.

Identification of rainfall homogenous regions in Saudi Arabia for experimenting and improving trend detection techniques.

In Saudi Arabia, identifying homogenous zones based on rainfall patterns is critical for ensuring a predictable and stable water resource and agriculture management strategy. As a result, the present research aims to identify Saudi Arabia's homogeneous rainfall zones and examine rainfall patterns in these areas. By proposing a novel trend analysis technique with a particular graphical representation, this study utilises and compares the traditional Mann-Kendall (MK) test, modified MK test, and basic Sen-innovative trend analysis (ITA) method. Another approach is to use the Pettit change point test to objectively identify subcategories as "low" or "high." The applications are based on 40-year rainfall records from 22 Saudi Arabian meteorological sites. K-means clustering and hierarchical clustering on principle component analysis (HCPC) were used to find homogeneous areas. The results of the homogeneous region identification revealed that the research area is divided into three clusters, each with three distinct climatic characteristics. Cluster 1 contains eight stations, whereas clusters 2 and 3 each have seven. The results of trend identification utilising the MK, MMK, and ITA tests revealed that cluster 1 had a falling rainfall trend, whereas cluster 2 had a very minor decreasing and increasing rainfall trend. Cluster 2 can be thought of as a transition zone. Cluster 3 observed an upward trend in rainfall. While the proposed new form of ITA produced similar results with more detailed analysis such as change point-based high and low value identification, and magnitude of decreasing and increasing trend, the proposed new form of ITA produced similar results with more detailed analysis such as change point-based high and low value identification. This study will serve as a foundation for future work by scientists and planners on the identification of climatic zones, the development of trend detection techniques, and the formulation of water resource management strategies.

Temporal-Spatial changes of monthly vegetation growth and their driving forces in the ancient Yellow river irrigation system, China.

Irrigation systems play vital roles not only in food production but also in supporting ecosystems. Understanding how the ecosystem has evolved in response to human activities is crucial for sustainable food production, especially for arid and semi-arid regions. In this study, we examined the trends of vegetation growth on a monthly basis in the ancient Yellow River irrigation system in Ningxia, China. We used the leaf area index (LAI) to characterize the vegetation growth from 2007 to 2019. The LAI trends were associated with a series of driving forces, explaining the spatial and temporal change of vegetation growth. With the provision of the Wilks feature importance method, 2-month averaged air temperature and irrigation were identified as the two most important variables for monthly LAI simulation. Future climate projections based on the Regional Climate Model system (RegCM) suggested dryer and longer summers under the RCP 8.5 scenario. These changes will increase the crop water demand during the growing months. In the future, water conflict might be further intensified in May, in which the present irrigation water has already led to a decreased crop growth. Our findings demonstrated that the Mann Kendall monthly trend analysis could provide more helpful information for monitoring the vegetation growth than the trend analysis on a yearly and seasonal basis.

Analysis of Roadkill on the Korean Expressways from 2004 to 2019.

Expressways in Korea are high-speed traffic roads connecting important cities. Road infrastructure continues to expand to accommodate the increase in traffic volume associated with the growth of industry and tourism. Here, data on 36,863 roadkill events that occurred on expressway routes managed by the Korea Expressway Corporation between 2004 and 2019 were analyzed. Characterizing patterns of roadkill is important for prioritizing roadkill mitigation measures. We identified consistently increasing or decreasing trends using Mann-Kendall statistics and Sen's slope. Roadkill was most common in Gangwon Province and was concentrated between May and June and between October and December. Water deer (Hydropotes inermis) was the most common road-killed species. The trend analysis revealed a statistically significant decline in Gangwon Province and a statistically significant increase in the Capital Area and Chungnam Province. There was a significant increase in wild boar (Sus scrofa) roadkill in the first and fourth quarters. Mitigation measures are needed for regions and species showing increasing trends, including water deer in the first to third quarters, periods for which no decline in water deer roadkill was noted.

Major Natural Disasters in China, 1985-2014: Occurrence and Damages.

This study aimed to describe the characteristics of natural disasters and associated losses from 1985 to 2014. The Mann-Kendall method was used to detect any long-term trends and abrupt changes. Hotspot analysis was conducted to detect the spatial clusters of disasters. We found an increasing trend in the occurrence of integrated natural disasters (tau = 0.594, p < 0.001), particularly for floods (tau = 0.507, p < 0.001), landslides (tau = 0.365, p = 0.009) and storms (tau = 0.289, p = 0.032). Besides, there was an abrupt increase of natural disasters in 1998-2000. Hotspots of droughts, floods, landslides and storms were identified in central, southern, southwest and southeast areas of China, respectively. Annual deaths from integrated natural disasters were decreasing (tau = -0.237, p = 0.068) at about 32 persons/year, decreasing at 17 persons/year for floods (tau = -0.154, p = 0.239), and decreasing at approximately 12 persons/year for storms (tau = -0.338, p = 0.009). No significant trend was detected in inflation-adjusted damages while a declining trend was detected in the ratio of year damage against GDP (gross domestic product). In conclusion, there has been an increasing trend in occurrence of natural disasters in China with the absence of an increase in life and economic losses. Despite the progress in the disaster adaption, there will be great challenges in disaster control for China in the future.

Temporal and spatial characteristics of the water pollutant concentration in Huaihe River Basin from 2003 to 2012, China.

Based on the monitoring data of 78 monitoring stations from 2003 to 2012, five key water quality indexes (biochemical oxygen demand: BOD5, permanganate index: CODMn, dissolved oxygen: DO, ammonium nitrogen: NH3-N, and total phosphorus: TP) were selected to analyze their temporal and spatial characteristics in the highly disturbed Huaihe River Basin via Mann-Kendall trend analysis and boxplot analysis. The temporal and spatial variations of water pollutant concentrations in the Huaihe River Basin were investigated and analyzed to provide a scientific basis for water pollution control, water environment protection, and ecological restoration. The results indicated that the Yinghe River, Quanhe River, Honghe River, Guohe River, and Baohe River were the most seriously polluted rivers, followed by Hongze Lake, Luoma Lake, Yishuhe River, and Nansi Lake. BOD5, CODMn, and NH3-N were the major pollution indexes, for which the monitoring stations reported that more than 40 % of the water quality concentrations exceeded the class IV level. There were 21, 50, 36, and 21 monitoring stations that recorded significantly decreasing trends for BOD5, CODMn, NH3-N, and TP, respectively, and 39 monitoring stations showed a significantly increasing trend for DO. Moreover, the water quality concentrations had a certain concentricity and volatility according to boxplot analysis for the 20 monitoring stations. The majority of monitoring stations recorded a large fluctuation for the monitoring indexes in 2003 and 2004, which indicated that the water quality concentrations were unstable. According to the seasonal variations of the water quality concentrations in the mainstream of Huaihe River, the monthly variation trends of the BOD5, CODMn, DO, NH3-N, and TP concentrations were basically consistent among the seven monitoring stations. The BOD5, CODMn, NH3-N, and TP concentrations were affected by the change of the stream discharge; changes in DO and NH3-N concentrations were influenced by the regional environmental temperature, and the DO and NH3-N concentrations decreased when the water temperature increased.

Hydroclimatic and water quality trends across three Mediterranean river basins.

Water resources are under pressure from multiple anthropogenic stressors such as changing climate, agriculture and water abstraction. This holds, in particular, for the Mediterranean region, where substantial changes in climate are expected throughout the 21st century. Nonetheless, little attention has been paid to linkages between long-term trends in climate, streamflow and water quality in Mediterranean river basins. In the present study, we perform a comparative analysis of recent trends in hydroclimatic parameters and nitrate pollution in three climatologically different Mediterranean watersheds (i.e., the Adige, Ebro and Sava River Basins). Mann-Kendall trend analyses of annual mean temperature, precipitation and streamflow (period 1971 to 2010) and monthly nitrate concentrations, mass fluxes and flow-adjusted concentrations (period 1996 to 2012) were performed in these river basins. Temperature is shown to have increased the most in the Ebro followed by the Sava, whereas minor increases are observed in the Adige. Precipitation presents, overall, a negative trend in the Ebro and a positive trend in both the Adige and Sava. These climatic trends thus suggest the highest risk of increasing water scarcity for the Ebro and the lowest risk for the Adige. This is confirmed by trend analyses of streamflow time series, which indicate a severe decline in streamflow for the Ebro and a substantial decline in the Sava, as opposed to the Adige showing no prevailing trend. Concerning surface water quality, nitrate pollution appears to have decreased in all study basins. Overall, these findings emphasize progressive reduction of water resources availability in river basins characterized by continental climate (i.e., Ebro and Sava). This study thus underlines the need for adapted river management in the Mediterranean region, particularly considering strong feedbacks between hydroclimatic trends, freshwater ecosystem services and water resources availability for agriculture, water supply and hydropower generation.

Spatial and temporal variability of fluoride concentrations in groundwater resources of Larestan and Gerash regions in Iran from 2003 to 2010.

There is discrepancy about intervals of fluoride monitoring in groundwater resources by Iranian authorities. Spatial and temporal variability of fluoride in groundwater resources of Larestan and Gerash regions in Iran were analyzed from 2003 to 2010 using a geospatial information system and the Mann-Kendall trend test. The mean concentrations of fluoride for the 8-year period in the eight cities and 31 villages were 1.6 and 2.0 mg/l, respectively; the maximum values were 2.4 and 3.8 mg/l, respectively. Spatial, temporal, and spatiotemporal variability of fluoride in overall groundwater resources were relatively constant over the years. However, results of the Mann-Kendall trend test revealed a monotonic trend in the time series of one city and 11 villages for the 8-year period. Specifically, one city and three villages showed positive significant Kendall's Tau values, suggesting an upward trend in fluoride concentrations over the 8-year period. In contrast, seven villages displayed negative significant Kendall's Tau values, arguing for a downward trend in fluoride concentrations over the years. From 2003 to 2010, approximately 52 % of the Larestan and Gerash areas have had fluoride concentrations above the maximum permissible Iranian drinking water standard fluoride level (1.4 mg/l), and about 116,000 people were exposed to such excess amounts. Therefore, our study supports for a close monitoring of fluoride concentrations from health authorities in monthly intervals, especially in villages and cities that showed positive trend in fluoride concentrations. Moreover, we recommend simultaneous implementation of cost-effective protective measures or interventions until a standard fluoride level is achieved.

Environmental response of an Irish estuary to changing land management practices.

Anthropogenic pressures have led to problems of nutrient over-enrichment and eutrophication in estuarine and coastal systems on a global scale. Recent improvements in farming practices, specifically a decrease in fertiliser application rates, have reduced nutrient loadings in Ireland. In line with national and European Directives, monitoring of Irish estuarine systems has been conducted for the last 30years, allowing a comparison of the effectiveness of measures undertaken to improve water quality and chemical and biological trends. The Blackwater Estuary, which drains a large agricultural catchment on the south coast of Ireland, has experienced a decrease in calculated nitrogen (N) (17%) and phosphorus (P) (20%) loads in the last decade. Monitored long-term river inputs reflect the reductions while estuarine P concentrations, chlorophyll and dissolved oxygen saturation show concurrent improvement. Consistently high N concentrations suggest a decoupling between N loads and estuarine responses. This highlights the complex interaction between N and P load reductions, and biochemical processes relating to remineralisation and primary production which can alter the effectiveness of the estuarine filter in reducing nutrient transport to the coastal zone. Effective management and reduction of both diffuse and point nutrient sources to surface waters require a consideration of the processes which may alter the effectiveness of measures in estuarine and coastal waters.