Groundwater Assessment Platform (GAP)

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array(25 items)
   0 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=30957, pid=124)
      originalId => protected30957 (integer)
      authors => protected'Araya, D.; Podgorski, J.; Berg, M.' (49 chars)
      title => protected'Groundwater salinity in the Horn of Africa: spatial prediction modeling and 
         estimated people at risk' (100 chars)
      journal => protected'Environment International' (25 chars)
      year => protected2023 (integer)
      volume => protected176 (integer)
      issue => protected'' (0 chars)
      startpage => protected'107925 (12 pp.)' (15 chars)
      otherpage => protected'' (0 chars)
      categories => protected'drinking water; groundwater quality; water scarcity; human health; Djibouti;
          Eritrea; Ethiopia; Kenya; Somalia; spatial modeling; machine learning; rand
         om forest' (161 chars)
      description => protected'<em>Background:</em> Changes in climate and anthropogenic activities have ma
         de water salinization a significant threat worldwide, affecting biodiversity
         , crop productivity and contributing to water insecurity. The Horn of Africa
         , which includes eastern Ethiopia, northeast Kenya, Eritrea, Djibouti, and S
         omalia, has natural characteristics that favor high groundwater salinity. Ex
         cess salinity has been linked to infrastructure and health problems, includi
         ng increased infant mortality. This region has suffered successive droughts 
         that have limited the availability of safe drinking water resources, leading
          to a humanitarian crisis for which little spatially explicit information ab
         out groundwater salinity is available.<br /><em>Methods:</em> Machine learni
         ng (random forest) is used to make spatial predictions of salinity levels at
          three electrical conductivity (EC) thresholds using data from 8646 borehole
         s and wells along with environmental predictor variables. Attention is paid 
         to understanding the input data, balancing classes, performing many iteratio
         ns, specifying cut-off values, employing spatial cross-validation, and ident
         ifying spatial uncertainties.<br /><em>Results:</em> Estimates are made for 
         this transboundary region of the population potentially exposed to hazardous
          salinity levels. The findings indicate that about 11.6 million people (∼7
         % of the total population), including 400,000 infants and half a million pre
         gnant women, rely on groundwater for drinking and live in areas of high grou
         ndwater salinity (EC &gt; 1500 µS/cm). Somalia is the most affected and h
         as the largest number of people potentially exposed. Around 50% of the Somal
         i population (5 million people) may be exposed to unsafe salinity levels in 
         their drinking water. In only five of Somalia's 18 regions are less than 50%
          of infants potentially exposed to unsafe salinity levels. The main drivers 
         of high salinity include precipitation, groundwater recharge, evaporation, o
         cean proximity, and frac...' (2633 chars)
      serialnumber => protected'0160-4120' (9 chars)
      doi => protected'10.1016/j.envint.2023.107925' (28 chars)
      uid => protected30957 (integer)
      _localizedUid => protected30957 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected30957 (integer)modified
      pid => protected124 (integer)
   1 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=26216, pid=124)
      originalId => protected26216 (integer)
      authors => protected'de Meyer,&nbsp;C.&nbsp;M.&nbsp;C.; Wahnfried,&nbsp;I.; Rodriguez Rodriguez,&
         nbsp;J.&nbsp;M.; Kipfer,&nbsp;R.; García Avelino,&nbsp;P.&nbsp;A.; Carpio D
         eza,&nbsp;E.&nbsp;A.; Berg,&nbsp;M.' (187 chars)
      title => protected'Hotspots of geogenic arsenic and manganese contamination in groundwater of t
         he floodplains in lowland Amazonia (South America)' (126 chars)
      journal => protected'Science of the Total Environment' (32 chars)
      year => protected2023 (integer)
      volume => protected860 (integer)
      issue => protected'' (0 chars)
      startpage => protected'160407 (14 pp.)' (15 chars)
      otherpage => protected'' (0 chars)
      categories => protected'holocene aquifers; Amazon river; hydrochemistry; drinking water; Peru; Brazi
         l' (77 chars)
      description => protected'Arsenic enrichment in groundwater resources in deltas and floodplains of lar
         ge sediment-rich rivers is a worldwide natural hazard to human health. High 
         spatial variability of arsenic concentrations in affected river basins limit
         s cost-effective mitigation strategies. Linking the chemical composition of 
         groundwater with the topography and fluvial geomorphology is a promising app
         roach for predicting arsenic pollution on a regional scale. Here we correlat
         e the distribution of arsenic contaminated wells with the fluvial dynamics i
         n the Amazon basin. Groundwater was sampled from tube wells along the Amazon
          River and its main tributaries in three distinct regions in Peru and Brazil
         . For each sample, the major and trace element concentrations were analyzed,
          and the position of the well within the sedimentary structure was determine
         d. The results show that aquifers in poorly weathered sediments deposited by
          sediment-rich rivers are prone to mobilization and accumulation of aqueous 
         arsenic and manganese, both in sub-Andean foreland basins, and in floodplain
         s downstream. Two zones at risk are distinguished: aquifers in the channel-d
         ominated part of the floodplain (CDF) and aquifers in the overbank deposits 
         on the less-dynamic part of the floodplain (LDF). Some 70 % of the wells loc
         ated on the CDF and 20 % on the LDF tap groundwater at concentrations exceed
         ing the WHO guideline of 10 μg/L arsenic (max. 430 μg/L), and 70 % (CDF) a
         nd 50 % (LDF) exceeded 0.4 mg/L manganese (max. 6.6 mg/L). None of the water
          samples located outside the actual floodplain of sediment-rich rivers, or o
         n riverbanks of sediment-poor rivers exceed 5 μg/L As, and only 4 % exceede
         d 0.4 mg/L Mn. The areas of highest risk can be delineated using satellite i
         magery. We observe similar patterns as in affected river basins in South and
          Southeast Asia indicating a key role of sedimentation processes and fluvial
          geomorphology in priming arsenic and manganese contamination in aquifers.' (1974 chars)
      serialnumber => protected'0048-9697' (9 chars)
      doi => protected'10.1016/j.scitotenv.2022.160407' (31 chars)
      uid => protected26216 (integer)
      _localizedUid => protected26216 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected26216 (integer)modified
      pid => protected124 (integer)
   2 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=24949, pid=124)
      originalId => protected24949 (integer)
      authors => protected'Ling,&nbsp;Y.; Podgorski,&nbsp;J.; Sadiq,&nbsp;M.; Rasheed,&nbsp;H.; Eqani,&
         nbsp;S.&nbsp;A.&nbsp;M.&nbsp;A.&nbsp;S.; Berg,&nbsp;M.' (130 chars)
      title => protected'Monitoring and prediction of high fluoride concentrations in groundwater in 
         Pakistan' (84 chars)
      journal => protected'Science of the Total Environment' (32 chars)
      year => protected2022 (integer)
      volume => protected839 (integer)
      issue => protected'' (0 chars)
      startpage => protected'156058 (9 pp.)' (14 chars)
      otherpage => protected'' (0 chars)
      categories => protected'aquifers; geogenic groundwater pollution; drinking water quality; human heal
         th threat; fluorosis; random forest modeling' (120 chars)
      description => protected'Concentrations of naturally occurring fluoride in groundwater exceeding the 
         WHO guideline of 1.5 mg/L have been detected in many parts of Pakistan. This
          may lead to dental or skeletal fluorosis and thereby poses a potential thre
         at to public health. Utilizing a total of 5483 fluoride concentrations, comp
         rising 2160 new measurements as well as those from other sources, we have ap
         plied machine learning techniques to predict the probability of fluoride in 
         groundwater in Pakistan exceeding 1.5 mg/L at a 250 m spatial resolution. Cl
         imate, soil, lithology, topography, and land cover parameters were identifie
         d as effective predictors of high fluoride concentrations in groundwater. Ex
         cellent model performance was observed in a random forest model that achieve
         d an Area Under the Curve (AUC) of 0.92 on test data that were not used in m
         odeling. The highest probabilities of high fluoride concentrations in ground
         water are predicted in the Thar Desert, Sargodha Division, and scattered alo
         ng the Sulaiman Mountains. Applying the model predictions to the population 
         density and accounting for groundwater usage in both rural and urban areas, 
         we estimate that about 13 million people may be at risk of fluorosis due to 
         consuming groundwater with fluoride concentrations &gt;1.5 mg/L in Pakistan,
          which corresponds to ~6% of the total population. Both the fluoride predict
         ion map and the health risk map can be used as important decision-making too
         ls for authorities and water resource managers in the identification and mit
         igation of groundwater fluoride contamination.' (1566 chars)
      serialnumber => protected'0048-9697' (9 chars)
      doi => protected'10.1016/j.scitotenv.2022.156058' (31 chars)
      uid => protected24949 (integer)
      _localizedUid => protected24949 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected24949 (integer)modified
      pid => protected124 (integer)
   3 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=25552, pid=124)
      originalId => protected25552 (integer)
      authors => protected'Podgorski,&nbsp;J.; Berg,&nbsp;M.' (33 chars)
      title => protected'Global analysis and prediction of fluoride in groundwater' (57 chars)
      journal => protected'Nature Communications' (21 chars)
      year => protected2022 (integer)
      volume => protected13 (integer)
      issue => protected'1' (1 chars)
      startpage => protected'4232 (9 pp.)' (12 chars)
      otherpage => protected'' (0 chars)
      categories => protected'' (0 chars)
      description => protected'The health of millions of people worldwide is negatively impacted by chronic
          exposure to elevated concentrations of geogenic fluoride in groundwater. Du
         e to health effects including dental mottling and skeletal fluorosis, the Wo
         rld Health Organization maintains a maximum guideline of 1.5 mg/L in drink
         ing water. As groundwater quality is not regularly tested in many areas, it 
         is often unknown if the water in a given well or spring contains harmful lev
         els of fluoride. Here we present a state-of-the-art global fluoride hazard m
         ap based on machine learning and over 400,000 fluoride measurements (10% of 
         which &gt;1.5 mg/L), which is then used to estimate the human population a
         t risk. Hotspots indicated by the groundwater fluoride hazard map include pa
         rts of central Australia, western North America, eastern Brazil and many are
         as of Africa and Asia. Of the approximately 180 million people potentially a
         ffected worldwide, most reside in Asia (51–59% of total) and Africa (37-46
         % of total), with the latter representing 6.5% of the continent’s populati
         on. Africa also contains 14 of the top 20 affected countries in terms of pop
         ulation at risk. We also illuminate and discuss the key globally relevant hy
         drochemical and environmental factors related to fluoride accumulation.' (1287 chars)
      serialnumber => protected'' (0 chars)
      doi => protected'10.1038/s41467-022-31940-x' (26 chars)
      uid => protected25552 (integer)
      _localizedUid => protected25552 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected25552 (integer)modified
      pid => protected124 (integer)
   4 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=24847, pid=124)
      originalId => protected24847 (integer)
      authors => protected'Araya,&nbsp;D.; Podgorski,&nbsp;J.; Berg,&nbsp;M.' (49 chars)
      title => protected'How widespread is fluoride contamination of Ghana's groundwater?' (64 chars)
      journal => protected'Water Science Policy' (20 chars)
      year => protected2022 (integer)
      volume => protected0 (integer)
      issue => protected'' (0 chars)
      startpage => protected'(4 pp.)' (7 chars)
      otherpage => protected'' (0 chars)
      categories => protected'' (0 chars)
      description => protected'' (0 chars)
      serialnumber => protected'' (0 chars)
      doi => protected'10.53014/OGJS9699' (17 chars)
      uid => protected24847 (integer)
      _localizedUid => protected24847 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected24847 (integer)modified
      pid => protected124 (integer)
   5 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=24363, pid=124)
      originalId => protected24363 (integer)
      authors => protected'Araya,&nbsp;D.; Podgorski,&nbsp;J.; Kumi,&nbsp;M.; Mainoo,&nbsp;P.&nbsp;A.; 
         Berg,&nbsp;M.' (89 chars)
      title => protected'Fluoride contamination of groundwater resources in Ghana: country-wide hazar
         d modeling and estimated population at risk' (119 chars)
      journal => protected'Water Research' (14 chars)
      year => protected2022 (integer)
      volume => protected212 (integer)
      issue => protected'' (0 chars)
      startpage => protected'118083 (10 pp.)' (15 chars)
      otherpage => protected'' (0 chars)
      categories => protected'Africa; drinking water resources; human health; fluorosis; random forest mod
         eling; groundwater pollution; geogenic contamination' (128 chars)
      description => protected'Most people in Ghana have no or only basic access to safely managed water. E
         specially in rural areas, much of the population relies on groundwater for d
         rinking, which can be contaminated with fluoride and lead to dental fluorosi
         s. Children under the age of two are particularly susceptible to the adverse
          effects of fluoride and can retain 80-90% of a fluoride dose, compared to 6
         0% in adults. Despite numerous local studies, no spatially continuous pictur
         e exists of the fluoride contamination across Ghana, nor is there any estima
         te of what proportion of the population is potentially exposed to unsafe flu
         oride levels. Here, we spatially model the probability of fluoride concentra
         tions exceeding 1.0 mg/L in groundwater across Ghana to identify risk areas
          and estimate the number of children and adults exposed to unsafe fluoride l
         evels in drinking water. We use a set of geospatial predictor variables with
          random forest modeling and evaluate the model performance through spatial c
         ross-validation. We found that approximately 15% of the area of Ghana, mainl
         y in the northeast, has a high probability of fluoride contamination. The to
         tal at-risk population is about 920,000 persons, or 3% of the population, wi
         th an estimated 240,000 children (0-9 years) in at-risk areas. In some distr
         icts, such as Karaga, Gushiegu, Tamale and Mion, 4 out of 10 children are po
         tentially exposed to fluoride poisoning. Geology and high evapotranspiration
          are the main drivers of fluoride enrichment in groundwater. Consequently, c
         limate change might put even greater pressure on the area's water resources.
          Our hazard maps should raise awareness and understanding of geogenic fluori
         de contamination in Ghana and can advise decision making at local levels to 
         avoid or mitigate fluoride-related risks.' (1789 chars)
      serialnumber => protected'0043-1354' (9 chars)
      doi => protected'10.1016/j.watres.2022.118083' (28 chars)
      uid => protected24363 (integer)
      _localizedUid => protected24363 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected24363 (integer)modified
      pid => protected124 (integer)
   6 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=24819, pid=124)
      originalId => protected24819 (integer)
      authors => protected'Podgorski,&nbsp;J.; Araya,&nbsp;D.; Berg,&nbsp;M.' (49 chars)
      title => protected'Geogenic manganese and iron in groundwater of Southeast Asia and Bangladesh 
         - machine learning spatial prediction modeling and comparison with arsenic' (150 chars)
      journal => protected'Science of the Total Environment' (32 chars)
      year => protected2022 (integer)
      volume => protected833 (integer)
      issue => protected'' (0 chars)
      startpage => protected'155131 (11 pp.)' (15 chars)
      otherpage => protected'' (0 chars)
      categories => protected'groundwater quality; drinking water; human health; random forest modeling; g
         eneralized boosted regression modeling; reducing groundwater' (136 chars)
      description => protected'Naturally occurring, geogenic manganese (Mn) and iron (Fe) are frequently fo
         und dissolved in groundwater at concentrations that make the water difficult
          to use (deposits, unpleasant taste) or, in the case of Mn, a potential heal
         th hazard. Over 6000 groundwater measurements of Mn and Fe in Southeast Asia
          and Bangladesh were assembled and statistically examined with other physico
         chemical parameters. The machine learning methods random forest and generali
         zed boosted regression modeling were used with spatially continuous environm
         ental parameters (climate, geology, soil, topography) to model and map the p
         robability of groundwater Mn &gt; 400 μg/L and Fe &gt; 0.3 mg/L for Southea
         st Asia and Bangladesh. The modeling indicated that drier climatic condition
         s are associated with a tendency of elevated Mn concentrations, whereas high
          Fe concentrations tend to be found in a more humid climate with elevated le
         vels of soil organic carbon. The spatial distribution of Mn &gt; 400 μg/L a
         nd Fe &gt; 0.3 mg/L was compared and contrasted with that of the critical ge
         ogenic contaminant arsenic (As), confirming that high Fe concentrations are 
         often associated with high As concentrations, whereas areas of high concentr
         ations of Mn and As are frequently found adjacent to each other. The probabi
         lity maps draw attention to areas prone to elevated concentrations of geogen
         ic Mn and Fe in groundwater and can help direct efforts to mitigate their ne
         gative effects. The greatest Mn hazard is found in densely populated northwe
         st Bangladesh and the Mekong, Red and Ma River Deltas of Cambodia and Vietna
         m. Widespread elevated Fe concentrations and their associated negative effec
         ts on water infrastructure pose challenges to water supply. The Mn and Fe pr
         ediction maps demonstrate the value of machine learning for the geospatial p
         rediction modeling and mapping of groundwater contaminants as well as the po
         tential for further constituents to be targeted by this novel approach.' (1971 chars)
      serialnumber => protected'0048-9697' (9 chars)
      doi => protected'10.1016/j.scitotenv.2022.155131' (31 chars)
      uid => protected24819 (integer)
      _localizedUid => protected24819 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected24819 (integer)modified
      pid => protected124 (integer)
   7 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=24157, pid=124)
      originalId => protected24157 (integer)
      authors => protected'Alam,&nbsp;M.&nbsp;F.; Villholth,&nbsp;K.&nbsp;G.; Podgorski,&nbsp;J.' (69 chars)
      title => protected'Human arsenic exposure risk via crop consumption and global trade from groun
         dwater-irrigated areas' (98 chars)
      journal => protected'Environmental Research Letters' (30 chars)
      year => protected2021 (integer)
      volume => protected16 (integer)
      issue => protected'12' (2 chars)
      startpage => protected'124013 (18 pp.)' (15 chars)
      otherpage => protected'' (0 chars)
      categories => protected'arsenic; groundwater; irrigation; exposure' (42 chars)
      description => protected'While drinking water is known to create significant health risk in arsenic h
         azard areas, the role of exposure to arsenic through food intake is less wel
         l understood, including the impact of food trade. Using the best available d
         atasets on crop production, irrigation, groundwater arsenic hazard, and inte
         rnational crop trade flows, we estimate that globally 17.2% of irrigated har
         vested area (or 45.2 million hectares) of 42 main crops are grown in arsenic
          hazard areas, contributing 19.7% of total irrigated crop production, or 418
          million metric tons (MMT) per year of these crops by mass. Two-thirds of th
         is area is dedicated to the major staple crops of rice, wheat, and maize (RW
         M) and produces 158 MMT per year of RWM, which is 8.0% of the total RWM prod
         uction and 18% of irrigated production. More than 25% of RWM consumed in the
          South Asian countries of India, Pakistan, and Bangladesh, where both arseni
         c hazard and degree of groundwater irrigation are high, originate from arsen
         ic hazard areas. Exposure to arsenic risk from crops also comes from interna
         tional trade, with 10.6% of rice, 2.4% of wheat, and 4.1% of maize trade flo
         ws coming from production in hazard areas. Trade plays a critical role in re
         distributing risk, with the greatest exposure risk borne by countries with a
          high dependence on food imports, particularly in the Middle East and small 
         island nations for which all arsenic risk in crops is imported. Intensifying
          climate variability and population growth may increase reliance on groundwa
         ter irrigation, including in arsenic hazard areas. Results show that RWM har
         vested area could increase by 54.1 million hectares (179% increase over curr
         ent risk area), predominantly in South and Southeast Asia. This calls for th
         e need to better understand the relative risk of arsenic exposure through fo
         od intake, considering the influence of growing trade and increased groundwa
         ter reliance for crop production.' (1933 chars)
      serialnumber => protected'1748-9326' (9 chars)
      doi => protected'10.1088/1748-9326/ac34bb' (24 chars)
      uid => protected24157 (integer)
      _localizedUid => protected24157 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected24157 (integer)modified
      pid => protected124 (integer)
   8 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=21041, pid=124)
      originalId => protected21041 (integer)
      authors => protected'Wu,&nbsp;R.; Podgorski,&nbsp;J.; Berg,&nbsp;M.; Polya,&nbsp;D.&nbsp;A.' (70 chars)
      title => protected'Geostatistical model of the spatial distribution of arsenic in groundwaters 
         in Gujarat State, India' (99 chars)
      journal => protected'Environmental Geochemistry and Health' (37 chars)
      year => protected2021 (integer)
      volume => protected43 (integer)
      issue => protected'' (0 chars)
      startpage => protected'2649' (4 chars)
      otherpage => protected'2664' (4 chars)
      categories => protected'groundwater; arsenic; health impacts; Gujarat; logistic regression; geostati
         stics' (81 chars)
      description => protected'Geogenic arsenic contamination in groundwaters poses a severe health risk to
          hundreds of millions of people globally. Notwithstanding the particular ris
         ks to exposed populations in the Indian sub-continent, at the time of writin
         g, there was a paucity of geostatistically based models of the spatial distr
         ibution of groundwater hazard in India. In this study, we used logistic regr
         ession models of secondary groundwater arsenic data with research-informed s
         econdary soil, climate and topographic variables as principal predictors gen
         erate hazard and risk maps of groundwater arsenic at a resolution of 1 km a
         cross Gujarat State. By combining models based on different arsenic concentr
         ations, we have generated a pseudo-contour map of groundwater arsenic concen
         trations, which indicates greater arsenic hazard (&gt; 10 μg/L) in the n
         orthwest, northeast and south-east parts of Kachchh District as well as nort
         hwest and southwest Banas Kantha District. The total number of people living
          in areas in Gujarat with groundwater arsenic concentration exceeding 10 μ
         g/L is estimated to be around 122,000, of which we estimate approximately 49
         ,000 people consume groundwater exceeding 10 µg/L. Using simple previously
          published dose–response relationships, this is estimated to have given ri
         se to 700 (prevalence) cases of skin cancer and around 10 cases of premature
          avoidable mortality/annum from internal (lung, liver, bladder) cancers—th
         at latter value is on the order of just 0.001% of internal cancers in Gujara
         t, reflecting the relative low groundwater arsenic hazard in Gujarat State.' (1595 chars)
      serialnumber => protected'0269-4042' (9 chars)
      doi => protected'10.1007/s10653-020-00655-7' (26 chars)
      uid => protected21041 (integer)
      _localizedUid => protected21041 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected21041 (integer)modified
      pid => protected124 (integer)
   9 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=20910, pid=124)
      originalId => protected20910 (integer)
      authors => protected'Podgorski,&nbsp;J.; Berg,&nbsp;M.' (33 chars)
      title => protected'Global threat of arsenic in groundwater' (39 chars)
      journal => protected'Science' (7 chars)
      year => protected2020 (integer)
      volume => protected368 (integer)
      issue => protected'6493' (4 chars)
      startpage => protected'845' (3 chars)
      otherpage => protected'850' (3 chars)
      categories => protected'' (0 chars)
      description => protected'Naturally occurring arsenic in groundwater affects millions of people worldw
         ide. We created a global prediction map of groundwater arsenic exceeding 10 
         micrograms per liter using a random forest machine-learning model based on 1
         1 geospatial environmental parameters and more than 50,000 aggregated data p
         oints of measured groundwater arsenic concentration. Our global prediction m
         ap includes known arsenic-affected areas and previously undocumented areas o
         f concern. By combining the global arsenic prediction model with household g
         roundwater-usage statistics, we estimate that 94 million to 220 million peop
         le are potentially exposed to high arsenic concentrations in groundwater, th
         e vast majority (94%) being in Asia. Because groundwater is increasingly use
         d to support growing populations and buffer against water scarcity due to ch
         anging climate, this work is important to raise awareness, identify areas fo
         r safe wells, and help prioritize testing.' (954 chars)
      serialnumber => protected'0036-8075' (9 chars)
      doi => protected'10.1126/science.aba1510' (23 chars)
      uid => protected20910 (integer)
      _localizedUid => protected20910 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected20910 (integer)modified
      pid => protected124 (integer)
   10 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=20587, pid=124)
      originalId => protected20587 (integer)
      authors => protected'Wallis,&nbsp;I.; Prommer,&nbsp;H.; Berg,&nbsp;M.; Siade,&nbsp;A.&nbsp;J.; Su
         n,&nbsp;J.; Kipfer,&nbsp;R.' (103 chars)
      title => protected'The river-groundwater interface as a hotspot for arsenic release' (64 chars)
      journal => protected'Nature Geoscience' (17 chars)
      year => protected2020 (integer)
      volume => protected13 (integer)
      issue => protected'' (0 chars)
      startpage => protected'288' (3 chars)
      otherpage => protected'295' (3 chars)
      categories => protected'' (0 chars)
      description => protected'Geogenic groundwater arsenic (As) contamination is pervasive in many aquifer
         s in south and southeast Asia. It is feared that recent increases in groundw
         ater abstractions could induce the migration of high-As groundwaters into pr
         eviously As-safe aquifers. Here we study an As-contaminated aquifer in Van P
         huc, Vietnam, located ~10 km southeast of Hanoi on the banks of the Red Ri
         ver, which is affected by large-scale groundwater abstraction. We used numer
         ical model simulations to integrate the groundwater flow and biogeochemical 
         reaction processes at the aquifer scale, constrained by detailed hydraulic, 
         environmental tracer, hydrochemical and mineralogical data. Our simulations 
         provide a mechanistic reconstruction of the anthropogenically induced spatio
         temporal variations in groundwater flow and biogeochemical dynamics and dete
         rmine the evolution of the migration rate and mass balance of As over severa
         l decades. We found that the riverbed–aquifer interface constitutes a biog
         eochemical reaction hotspot that acts as the main source of elevated As conc
         entrations. We show that a sustained As release relies on regular replenishm
         ent of river muds rich in labile organic matter and reactive iron oxides and
          that pumping-induced groundwater flow may facilitate As migration over dist
         ances of several kilometres into adjacent aquifers.' (1343 chars)
      serialnumber => protected'1752-0894' (9 chars)
      doi => protected'10.1038/s41561-020-0557-6' (25 chars)
      uid => protected20587 (integer)
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      _versionedUid => protected20587 (integer)modified
      pid => protected124 (integer)
   11 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=20114, pid=124)
      originalId => protected20114 (integer)
      authors => protected'Stopelli,&nbsp;E.; Duyen,&nbsp;V.&nbsp;T.; Mai,&nbsp;T.&nbsp;T.; Trang,&nbsp
         ;P.&nbsp;T.&nbsp;K.; Viet,&nbsp;P.&nbsp;H.; Lightfoot,&nbsp;A.; Kipfer,&nbsp
         ;R.; Schneider,&nbsp;M.; Eiche,&nbsp;E.; Kontny,&nbsp;A.; Neumann,&nbsp;T.; 
         Glodowska,&nbsp;M.; Patzner,&nbsp;M.; Kappler,&nbsp;A.; Kleindienst,&nbsp;S.
         ; Rathi,&nbsp;B.; Cirpka,&nbsp;O.; Bostick,&nbsp;B.; Prommer,&nbsp;H.; Winke
         l,&nbsp;L.&nbsp;H.&nbsp;E.; Berg,&nbsp;M.' (421 chars)
      title => protected'Spatial and temporal evolution of groundwater arsenic contamination in the R
         ed River delta, Vietnam: interplay of mobilisation and retardation processes' (152 chars)
      journal => protected'Science of the Total Environment' (32 chars)
      year => protected2020 (integer)
      volume => protected717 (integer)
      issue => protected'' (0 chars)
      startpage => protected'137143 (13 pp.)' (15 chars)
      otherpage => protected'' (0 chars)
      categories => protected'groundwater hydrochemistry; water isotopes; arsenic geochemistry; reductive 
         dissolution; redox transition; methanogenic conditions' (130 chars)
      description => protected'Geogenic arsenic (As) contamination of groundwater poses a major threat to g
         lobal health, particularly in Asia. To mitigate this exposure, groundwater i
         s increasingly extracted from low-As Pleistocene aquifers. This, however, di
         sturbs groundwater flow and potentially draws high-As groundwater into low-A
         s aquifers.<br /> Here we report a detailed characterisation of the Van Phuc
          aquifer in the Red River Delta region, Vietnam, where high-As groundwater f
         rom a Holocene aquifer is being drawn into a low-As Pleistocene aquifer. Thi
         s study includes data from eight years (2010–2017) of groundwater observat
         ions to develop an understanding of the spatial and temporal evolution of th
         e redox status and groundwater hydrochemistry.<br /> Arsenic concentrations 
         were highly variable (0.5-510 μg/L) over spatial scales of &lt;200 m. Five 
         hydro(geo)chemical zones (indicated as A to E) were identified in the aquife
         r, each associated with specific As mobilisation and retardation processes. 
         At the riverbank (zone A), As is mobilised from freshly deposited sediments 
         where Fe(III)-reducing conditions occur. Arsenic is then transported across 
         the Holocene aquifer (zone B), where the vertical intrusion of evaporative w
         ater, likely enriched in dissolved organic matter, promotes methanogenic con
         ditions and further release of As (zone C). In the redox transition zone at 
         the boundary of the two aquifers (zone D), groundwater arsenic concentration
         s decrease by sorption and incorporations onto Fe(II) carbonates and Fe(II)/
         Fe(III) (oxyhydr)oxides under reducing conditions. The sorption/incorporatio
         n of As onto Fe(III) minerals at the redox transition and in the Mn(IV)-redu
         cing Pleistocene aquifer (zone E) has consistently kept As concentrations be
         low 10 μg/L for the studied period of 2010–2017, and the location of the 
         redox transition zone does not appear to have propagated significantly. Yet,
          the largest temporal hydrochemical changes were found in the Pleistocene aq
         uifer caused by groundwa...' (2096 chars)
      serialnumber => protected'0048-9697' (9 chars)
      doi => protected'10.1016/j.scitotenv.2020.137143' (31 chars)
      uid => protected20114 (integer)
      _localizedUid => protected20114 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected20114 (integer)modified
      pid => protected124 (integer)
   12 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=18761, pid=124)
      originalId => protected18761 (integer)
      authors => protected'Podgorski,&nbsp;J.; Berg,&nbsp;M.; Kipfer,&nbsp;R.' (50 chars)
      title => protected'Isotope mapping of groundwater pollution and renewal' (52 chars)
      journal => protected'IAEA Bulletin' (13 chars)
      year => protected2019 (integer)
      volume => protected60 (integer)
      issue => protected'1' (1 chars)
      startpage => protected'31' (2 chars)
      otherpage => protected'32' (2 chars)
      categories => protected'' (0 chars)
      description => protected'An index aquifer vulnerability study from western Canada (left) compared wit
         h a new logistic regression map of these vulnerability index values on the o
         nline GAP platform (right). The red colour shows areas with the highest vuln
         erability. The green areas are less vulnerable or adequately protected from 
         surface contamination.' (326 chars)
      serialnumber => protected'' (0 chars)
      doi => protected'' (0 chars)
      uid => protected18761 (integer)
      _localizedUid => protected18761 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected18761 (integer)modified
      pid => protected124 (integer)
   13 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=17318, pid=124)
      originalId => protected17318 (integer)
      authors => protected'Podgorski,&nbsp;J.&nbsp;E.; Labhasetwar,&nbsp;P.; Saha,&nbsp;D.; Berg,&nbsp;
         M.' (78 chars)
      title => protected'Prediction modeling and mapping of groundwater fluoride contamination throug
         hout India' (86 chars)
      journal => protected'Environmental Science and Technology' (36 chars)
      year => protected2018 (integer)
      volume => protected52 (integer)
      issue => protected'17' (2 chars)
      startpage => protected'9889' (4 chars)
      otherpage => protected'9898' (4 chars)
      categories => protected'' (0 chars)
      description => protected'For about the past eight decades, high concentrations of naturally occurring
          fluoride have been detected in groundwater in different parts of India. The
          chronic consumption of fluoride in high concentrations is recognized to cau
         se dental and skeletal fluorosis. We have used the random forest machine-lea
         rning algorithm to model a data set of 12 600 groundwater fluoride concentra
         tions from throughout India along with spatially continuous predictor variab
         les of predominantly geology, climate, and soil parameters. Despite only sur
         face parameters being available to describe a subsurface phenomenon, this ha
         s produced a highly accurate prediction map of fluoride concentrations excee
         ding 1.5 mg/L at 1 km resolution throughout the country. The most affected a
         reas are the northwestern states/territories of Delhi, Gujarat, Haryana, Pun
         jab, and Rajasthan and the southern states of Andhra Pradesh, Karnataka, Tam
         il Nadu, and Telangana. The total number of people at risk of fluorosis due 
         to fluoride in groundwater is predicted to be around 120 million, or 9% of t
         he population. This number is based on rural populations and accounts for av
         erage rates of groundwater consumption from nonmanaged sources. The new fluo
         ride hazard and risk maps can be used by authorities in conjunction with det
         ailed groundwater utilization information to prioritize areas in need of mit
         igation measures.' (1385 chars)
      serialnumber => protected'0013-936X' (9 chars)
      doi => protected'10.1021/acs.est.8b01679' (23 chars)
      uid => protected17318 (integer)
      _localizedUid => protected17318 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected17318 (integer)modified
      pid => protected124 (integer)
   14 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=16022, pid=124)
      originalId => protected16022 (integer)
      authors => protected'Razanamahandry,&nbsp;L.&nbsp;C.; Andrianisa,&nbsp;H.&nbsp;A.; Karoui,&nbsp;H
         .; Podgorski,&nbsp;J.; Yacouba,&nbsp;H.' (115 chars)
      title => protected'Prediction model for cyanide soil pollution in artisanal gold mining area by
          using logistic regression' (102 chars)
      journal => protected'Catena' (6 chars)
      year => protected2018 (integer)
      volume => protected162 (integer)
      issue => protected'' (0 chars)
      startpage => protected'40' (2 chars)
      otherpage => protected'50' (2 chars)
      categories => protected'hazardous chemicals; catchment area; diffuse pollution; soil contamination; 
         risk assessment; Burkina Faso' (105 chars)
      description => protected'It has been reported that persistent cyanide pollution occurs in artisanal s
         mall-scale gold mining (ASGM)-affected catchment areas in Burkina Faso. In t
         he present study, the logistic regression method was employed to identify th
         e factors that influence the spatial distribution of cyanide pollution as we
         ll as to predict the cyanide pollution map risk at catchment level. Soil sam
         ples were collected from two ASGM sites in the northern Zougnazagmiline ("No
         rth") site and southern Galgouli ("South") site parts of Burkina Faso, cover
         ing areas of 22 km<sup>2</sup> and 20 km<sup>2</sup>, respectively. Free cya
         nide concentration in each sample was measured. It was shown that the spatia
         l distribution of cyanide was solely controlled by the soil type in Zougnaza
         gmiline and both the soil type and electric conductivity in Galgouli. On the
          other hand, the cyanidation zones within the two catchments were the places
          where the highest risk of cyanide pollution occurs, with probabilities of 0
         .8 and 1 in Zougnazagmiline and Galgouli, respectively. > 20% of the settled
          area in the Zougnazagmiline and 5% of that in Galgouli were exposed to cyan
         ide pollution. Logistic regression was able to reliably predict cyanide cont
         amination in areas affected by ASGM. The model could be useful for decision-
         makers to plan ASGM-site decontamination.' (1333 chars)
      serialnumber => protected'0341-8162' (9 chars)
      doi => protected'10.1016/j.catena.2017.11.018' (28 chars)
      uid => protected16022 (integer)
      _localizedUid => protected16022 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected16022 (integer)modified
      pid => protected124 (integer)
   15 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=17785, pid=124)
      originalId => protected17785 (integer)
      authors => protected'Bretzler,&nbsp;A.; Stolze,&nbsp;L.; Nikiema,&nbsp;J.; Lalanne,&nbsp;F.; Ghad
         iri,&nbsp;E.; Brennwald,&nbsp;M.&nbsp;S.; Rolle,&nbsp;M.; Schirmer,&nbsp;M.' (151 chars)
      title => protected'Hydrogeochemical and multi-tracer investigations of arsenic-affected aquifer
         s in semi-arid West Africa' (102 chars)
      journal => protected'Geoscience Frontiers' (20 chars)
      year => protected2019 (integer)
      volume => protected10 (integer)
      issue => protected'5' (1 chars)
      startpage => protected'1685' (4 chars)
      otherpage => protected'1699' (4 chars)
      categories => protected'arsenic; groundwater chemistry; West Africa; fractured aquifers; residence t
         ime; noble gases' (92 chars)
      description => protected'The semi-arid Sahel regions of West Africa rely heavily on groundwater from 
         shallow to moderately deep (&lt;100 m b.g.l.) crystalline bedrock aquifers f
         or drinking water production. Groundwater quality may be affected by high ge
         ogenic arsenic (As) concentrations (&gt;10 μg/L) stemming from the oxidatio
         n of sulphide minerals (pyrite, arsenopyrite) in mineralised zones. These aq
         uifers are still little investigated, especially concerning groundwater resi
         dence times and the influence of the annual monsoon season on groundwater ch
         emistry. To gain insights on the temporal aspects of As contamination, we ha
         ve used isotope tracers (noble gases, <sup>3</sup>H, stable water isotopes (
         <sup>2</sup>H, <sup>18</sup>O)) and performed hydrochemical analyses on grou
         ndwater abstracted from tube wells and dug wells in a small study area in so
         uthwestern Burkina Faso. Results revealed a great variability in groundwater
          properties (e.g. redox conditions, As concentrations, water level, residenc
         e time) over spatial scales of only a few hundred metres, characteristic of 
         the highly heterogeneous fractured underground. Elevated As levels are found
          in oxic groundwater of circum-neutral pH and show little relation with any 
         of the measured parameters. Arsenic concentrations are relatively stable ove
         r the course of the year, with little effect seen by the monsoon. Groundwate
         r residence time does not seem to have an influence on As concentrations, as
          elevated As can be found both in groundwater with short (&lt;50 a) and long
          (&gt;10<sup>3</sup> a) residence times as indicated by <sup>3</sup>He/<sup>
         4</sup>He ratios spanning three orders of magnitude. These results support t
         he hypothesis that the proximity to mineralised zones is the most crucial fa
         ctor controlling As concentrations in the observed redox/pH conditions. The 
         existence of very old water portions with residence times &gt;10<sup>3</sup>
          years already at depths of &lt;50 m b.g.l. is a new finding for the shallow
          fractured bedrock aquif...' (2126 chars)
      serialnumber => protected'1674-9871' (9 chars)
      doi => protected'10.1016/j.gsf.2018.06.004' (25 chars)
      uid => protected17785 (integer)
      _localizedUid => protected17785 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected17785 (integer)modified
      pid => protected124 (integer)
   16 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=15112, pid=124)
      originalId => protected15112 (integer)
      authors => protected'de Meyer,&nbsp;C.&nbsp;M.&nbsp;C.; Rodríguez,&nbsp;J.&nbsp;M.; Carpio,&nbsp
         ;E.&nbsp;A.; García,&nbsp;P.&nbsp;A.; Stengel,&nbsp;C.; Berg,&nbsp;M.' (146 chars)
      title => protected'Arsenic, manganese and aluminum contamination in groundwater resources of We
         stern Amazonia (Peru)' (97 chars)
      journal => protected'Science of the Total Environment' (32 chars)
      year => protected2017 (integer)
      volume => protected607 (integer)
      issue => protected'' (0 chars)
      startpage => protected'1437' (4 chars)
      otherpage => protected'1450' (4 chars)
      categories => protected'geogenic contamination; drinking water; trace elements; Amazon River; holoce
         ne; Iquitos' (87 chars)
      description => protected'This paper presents a first integrated survey on the occurrence and distribu
         tion of geogenic contaminants in groundwater resources of Western Amazonia i
         n Peru. An increasing number of groundwater wells have been constructed for 
         drinking water purposes in the last decades; however, the chemical quality o
         f the groundwater resources in the Amazon region is poorly studied. We colle
         cted groundwater from the regions of Iquitos and Pucallpa to analyze the hyd
         rochemical characteristics, including trace elements. The source aquifer of 
         each well was determined by interpretation of the available geological infor
         mation, which identified four different aquifer types with distinct hydroche
         mical properties. The majority of the wells in two of the aquifer types tap 
         groundwater enriched in aluminum, arsenic, or manganese at levels harmful to
          human health. Holocene alluvial aquifers along the main Amazon tributaries 
         with anoxic, near pH-neutral groundwater contained high concentrations of ar
         senic (up to 700 μg/L) and manganese (up to 4 mg/L). Around Iquitos, the ac
         idic groundwater (4.2 ≤ pH ≤ 5.5) from unconfined aquifers composed of p
         ure sand had dissolved aluminum concentrations of up to 3.3 mg/L. Groundwate
         r from older or deeper aquifers generally was of good chemical quality. The 
         high concentrations of toxic elements highlight the urgent need to assess th
         e groundwater quality throughout Western Amazonia.' (1418 chars)
      serialnumber => protected'0048-9697' (9 chars)
      doi => protected'10.1016/j.scitotenv.2017.07.059' (31 chars)
      uid => protected15112 (integer)
      _localizedUid => protected15112 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected15112 (integer)modified
      pid => protected124 (integer)
   17 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=15138, pid=124)
      originalId => protected15138 (integer)
      authors => protected'Bretzler,&nbsp;A.; Berg,&nbsp;M.; Winkel,&nbsp;L.; Amini,&nbsp;M.; Rodriguez
         -Lado,&nbsp;L.; Sovann,&nbsp;C.; Polya,&nbsp;D.&nbsp;A.; Johnson,&nbsp;A.' (149 chars)
      title => protected'Geostatistical modelling of arsenic hazard in groundwater' (57 chars)
      journal => protected'In: Bhattacharya,&nbsp;P.; Polya,&nbsp;D.&nbsp;A.; Jovanovic,&nbsp;D. (Eds.)
         , Best practice guide on the control of arsenic in drinking water' (141 chars)
      year => protected2017 (integer)
      volume => protected0 (integer)
      issue => protected'' (0 chars)
      startpage => protected'153' (3 chars)
      otherpage => protected'160' (3 chars)
      categories => protected'' (0 chars)
      description => protected'' (0 chars)
      serialnumber => protected'' (0 chars)
      doi => protected'' (0 chars)
      uid => protected15138 (integer)
      _localizedUid => protected15138 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected15138 (integer)modified
      pid => protected124 (integer)
   18 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=15232, pid=124)
      originalId => protected15232 (integer)
      authors => protected'Podgorski,&nbsp;J.&nbsp;E.; Eqani,&nbsp;S.&nbsp;A.&nbsp;M.&nbsp;A.&nbsp;S.; 
         Khanam,&nbsp;T.; Ullah,&nbsp;R.; Shen,&nbsp;H.; Berg,&nbsp;M.' (137 chars)
      title => protected'Extensive arsenic contamination in high-pH unconfined aquifers in the Indus 
         Valley' (82 chars)
      journal => protected'Science Advances' (16 chars)
      year => protected2017 (integer)
      volume => protected3 (integer)
      issue => protected'8' (1 chars)
      startpage => protected'e1700935 (10 pp.)' (17 chars)
      otherpage => protected'' (0 chars)
      categories => protected'' (0 chars)
      description => protected'Arsenic-contaminated aquifers are currently estimated to affect ~150 million
          people around the world. However, the full extent of the problem remains el
         usive. This is also the case in Pakistan, where previous studies focused on 
         isolated areas. Using a new data set of nearly 1200 groundwater quality samp
         les throughout Pakistan, we have created state-of-the-art hazard and risk ma
         ps of arsenic-contaminated groundwater for thresholds of 10 and 50 μg/liter
         . Logistic regression analysis was used with 1000 iterations, where surface 
         slope, geology, and soil parameters were major predictor variables. The haza
         rd model indicates that much of the Indus Plain is likely to have elevated a
         rsenic concentrations, although the rest of the country is mostly safe. Unli
         ke other arsenic-contaminated areas of Asia, the arsenic release process in 
         the arid Indus Plain appears to be dominated by elevated-pH dissolution, res
         ulting from alkaline topsoil and extensive irrigation of unconfined aquifers
         , although pockets of reductive dissolution are also present. We estimate th
         at approximately 50 million to 60 million people use groundwater within the 
         area at risk, with hot spots around Lahore and Hyderabad. This number is ala
         rmingly high and demonstrates the urgent need for verification and testing o
         f all drinking water wells in the Indus Plain, followed by appropriate mitig
         ation measures.' (1383 chars)
      serialnumber => protected'' (0 chars)
      doi => protected'10.1126/sciadv.1700935' (22 chars)
      uid => protected15232 (integer)
      _localizedUid => protected15232 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected15232 (integer)modified
      pid => protected124 (integer)
   19 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=14274, pid=124)
      originalId => protected14274 (integer)
      authors => protected'Bretzler,&nbsp;A.; Lalanne,&nbsp;F.; Nikiema,&nbsp;J.; Podgorski,&nbsp;J.; P
         fenninger,&nbsp;N.; Berg,&nbsp;M.; Schirmer,&nbsp;M.' (128 chars)
      title => protected'Groundwater arsenic contamination in Burkina Faso, West Africa: predicting a
         nd verifying regions at risk' (104 chars)
      journal => protected'Science of the Total Environment' (32 chars)
      year => protected2017 (integer)
      volume => protected584 (integer)
      issue => protected'' (0 chars)
      startpage => protected'958' (3 chars)
      otherpage => protected'970' (3 chars)
      categories => protected'arsenic contamination; drinking water; West Africa; sulphide minerals; hazar
         d modelling; health threat' (102 chars)
      description => protected'Arsenic contamination in groundwater from crystalline basement rocks in West
          Africa has only been documented in isolated areas and presents a serious he
         alth threat in a region already facing multiple challenges related to water 
         quality and scarcity. We present a comprehensive dataset of arsenic concentr
         ations from drinking water wells in rural Burkina Faso (n = 1498), of which 
         14.6% are above 10 μg/L. Included in this dataset are 269 new samples from 
         regions where no published water quality data existed. We used multivariate 
         logistic regression with arsenic measurements as calibration data and maps o
         f geology and mineral deposits as independent predictor variables to create 
         arsenic prediction models at concentration thresholds of 5, 10 and 50 μg/L.
          These hazard maps delineate areas vulnerable to groundwater arsenic contami
         nation in Burkina Faso. Bedrock composed of schists and volcanic rocks of th
         e Birimian formation, potentially harbouring arsenic-containing sulphide min
         erals, has the highest probability of yielding groundwater arsenic concentra
         tions > 10 μg/L. Combined with population density estimates, the arsenic pr
         ediction models indicate that ~ 560,000 people are potentially exposed to ar
         senic-contaminated groundwater in Burkina Faso. The same arsenic-bearing geo
         logical formations that are positive predictors for elevated arsenic concent
         rations in Burkina Faso also exist in neighbouring countries such as Mali, G
         hana and Ivory Coast. This study's results are thus of transboundary relevan
         ce and can act as a trigger for targeted water quality surveys and mitigatio
         n efforts.' (1606 chars)
      serialnumber => protected'0048-9697' (9 chars)
      doi => protected'10.1016/j.scitotenv.2017.01.147' (31 chars)
      uid => protected14274 (integer)
      _localizedUid => protected14274 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected14274 (integer)modified
      pid => protected124 (integer)
   20 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=7346, pid=124)
      originalId => protected7346 (integer)
      authors => protected'Rodríguez-Lado,&nbsp;L.; Sun,&nbsp;G.; Berg,&nbsp;M.; Zhang,&nbsp;Q.; Xue,&
         nbsp;H.; Zheng,&nbsp;Q.; Johnson,&nbsp;C.&nbsp;A.' (125 chars)
      title => protected'Groundwater arsenic contamination throughout China' (50 chars)
      journal => protected'Science' (7 chars)
      year => protected2013 (integer)
      volume => protected341 (integer)
      issue => protected'6148' (4 chars)
      startpage => protected'866' (3 chars)
      otherpage => protected'868' (3 chars)
      categories => protected'' (0 chars)
      description => protected'Arsenic-contaminated groundwater used for drinking in China is a health thre
         at that was first recognized in the 1960s. However, because of the sheer siz
         e of the country, millions of groundwater wells remain to be tested in order
          to determine the magnitude of the problem. We developed a statistical risk 
         model that classifies safe and unsafe areas with respect to geogenic arsenic
          contamination in China, using the threshold of 10 micrograms per liter, the
          World Health Organization guideline and current Chinese standard for drinki
         ng water. We estimate that 19.6 million people are at risk of being affected
          by the consumption of arsenic-contaminated groundwater. Although the result
         s must be confirmed with additional field measurements, our risk model ident
         ifies numerous arsenic-affected areas and highlights the potential magnitude
          of this health threat in China.' (868 chars)
      serialnumber => protected'0036-8075' (9 chars)
      doi => protected'10.1126/science.1237484' (23 chars)
      uid => protected7346 (integer)
      _localizedUid => protected7346 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected7346 (integer)modified
      pid => protected124 (integer)
   21 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=6600, pid=124)
      originalId => protected6600 (integer)
      authors => protected'Winkel,&nbsp;L.&nbsp;H.&nbsp;E.; Trang,&nbsp;P.&nbsp;T.&nbsp;K.; Lan,&nbsp;V
         .&nbsp;M.; Stengel,&nbsp;C.; Amini,&nbsp;M.; Ha,&nbsp;N.&nbsp;T.; Viet,&nbsp
         ;P.&nbsp;H.; Berg,&nbsp;M.' (178 chars)
      title => protected'Arsenic pollution of groundwater in Vietnam exacerbated by deep aquifer expl
         oitation for more than a century' (108 chars)
      journal => protected'Proceedings of the National Academy of Sciences of the United States of Amer
         ica PNAS' (84 chars)
      year => protected2011 (integer)
      volume => protected108 (integer)
      issue => protected'4' (1 chars)
      startpage => protected'1246' (4 chars)
      otherpage => protected'1251' (4 chars)
      categories => protected'three-dimensional risk modeling; anthropogenic influence; drinking water res
         ources; geogenic contamination; health threat' (121 chars)
      description => protected'Arsenic contamination of shallow groundwater is among the biggest health thr
         eats in the developing world. Targeting uncontaminated deep aquifers is a po
         pular mitigation option although its long-term impact remains unknown. Here 
         we present the alarming results of a large-scale groundwater survey covering
          the entire Red River Delta and a unique probability model based on three-di
         mensional Quaternary geology. Our unprecedented dataset reveals that ∼7 mi
         llion delta inhabitants use groundwater contaminated with toxic elements, in
         cluding manganese, selenium, and barium. Depth-resolved probabilities and ar
         senic concentrations indicate drawdown of arsenic-enriched waters from Holoc
         ene aquifers to naturally uncontaminated Pleistocene aquifers as a result of
          > 100 years of groundwater abstraction. Vertical arsenic migration induced 
         by large-scale pumping from deep aquifers has been discussed to occur elsewh
         ere, but has never been shown to occur at the scale seen here. The present s
         ituation in the Red River Delta is a warning for other As-affected regions w
         here groundwater is extensively pumped from uncontaminated aquifers underlyi
         ng high arsenic aquifers or zones.' (1174 chars)
      serialnumber => protected'0027-8424' (9 chars)
      doi => protected'10.1073/pnas.1011915108' (23 chars)
      uid => protected6600 (integer)
      _localizedUid => protected6600 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected6600 (integer)modified
      pid => protected124 (integer)
   22 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=5733, pid=124)
      originalId => protected5733 (integer)
      authors => protected'Amini,&nbsp;M.; Abbaspour,&nbsp;K.&nbsp;C.; Berg,&nbsp;M.; Winkel,&nbsp;L.; 
         Hug,&nbsp;S.&nbsp;J.; Hoehn,&nbsp;E.; Yang,&nbsp;H.; Johnson,&nbsp;C.&nbsp;A
         .' (153 chars)
      title => protected'Statistical modeling of global geogenic arsenic contamination in groundwater' (76 chars)
      journal => protected'Environmental Science and Technology' (36 chars)
      year => protected2008 (integer)
      volume => protected42 (integer)
      issue => protected'10' (2 chars)
      startpage => protected'3669' (4 chars)
      otherpage => protected'3675' (4 chars)
      categories => protected'' (0 chars)
      description => protected'Contamination of groundwaters with geogenic arsenic poses a major health ris
         k to millions of people. Although the main geochemical mechanisms of arsenic
          mobilization are well understood, the worldwide scale of affected regions i
         s still unknown. In this study we used a large database of measured arsenic 
         concentration in groundwaters (around 20,000 data points) from around the wo
         rld as well as digital maps of physical characteristics such as soil, geolog
         y, climate, and elevation to model probability maps of global arsenic contam
         ination. A novel rule-based statistical procedure was used to combine the ph
         ysical data and expert knowledge to delineate two process regions for arseni
         c mobilization: “reducing” and “high-pH/oxidizing”. Arsenic concentr
         ations were modeled in each region using regression analysis and adaptive ne
         uro-fuzzy inferencing followed by Latin hypercube sampling for uncertainty p
         ropagation to produce probability maps. The derived global arsenic models co
         uld benefit from more accurate geologic information and aquifer chemical/phy
         sical information. Using some proxy surface information, however, the models
          explained 77% of arsenic variation in reducing regions and 68% of arsenic v
         ariation in high-pH/oxidizing regions. The probability maps based on the abo
         ve models correspond well with the known contaminated regions around the wor
         ld and delineate new untested areas that have a high probability of arsenic 
         contamination. Notable among these regions are South East and North West of 
         China in Asia, Central Australia, New Zealand, Northern Afghanistan, and Nor
         thern Mali and Zambia in Africa.' (1628 chars)
      serialnumber => protected'0013-936X' (9 chars)
      doi => protected'10.1021/es702859e' (17 chars)
      uid => protected5733 (integer)
      _localizedUid => protected5733 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected5733 (integer)modified
      pid => protected124 (integer)
   23 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=5789, pid=124)
      originalId => protected5789 (integer)
      authors => protected'Amini,&nbsp;M.; Mueller,&nbsp;K.; Abbaspour,&nbsp;K.&nbsp;C.; Rosenberg,&nbs
         p;T.; Afyuni,&nbsp;M.; Møller,&nbsp;K.&nbsp;N.; Sarr,&nbsp;M.; Johnson,&nbs
         p;C.&nbsp;A.' (164 chars)
      title => protected'Statistical modeling of global geogenic fluoride contamination in groundwate
         rs' (78 chars)
      journal => protected'Environmental Science and Technology' (36 chars)
      year => protected2008 (integer)
      volume => protected42 (integer)
      issue => protected'10' (2 chars)
      startpage => protected'3662' (4 chars)
      otherpage => protected'3668' (4 chars)
      categories => protected'' (0 chars)
      description => protected'The use of groundwater with high fluoride concentrations poses a health thre
         at to millions of people around the world. This study aims at providing a gl
         obal overview of potentially fluoride-rich groundwaters by modeling fluoride
          concentration. A large database of worldwide fluoride concentrations as wel
         l as available information on related environmental factors such as soil pro
         perties, geological settings, and climatic and topographical information on 
         a global scale have all been used in the model. The modeling approach combin
         es geochemical knowledge with statistical methods to devise a rule-based sta
         tistical procedure, which divides the world into 8 different “process regi
         ons”. For each region a separate predictive model was constructed. The end
          result is a global probability map of fluoride concentration in the groundw
         ater. Comparisons of the modeled and measured data indicate that 60−70% of
          the fluoride variation could be explained by the models in six process regi
         ons, while in two process regions only 30% of the variation in the measured 
         data was explained. Furthermore, the global probability map corresponded wel
         l with fluorotic areas described in the international literature. Although t
         he probability map should not replace fluoride testing, it can give a first 
         indication of possible contamination and thus may support the planning proce
         ss of new drinking water projects.' (1402 chars)
      serialnumber => protected'0013-936X' (9 chars)
      doi => protected'10.1021/es071958y' (17 chars)
      uid => protected5789 (integer)
      _localizedUid => protected5789 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected5789 (integer)modified
      pid => protected124 (integer)
   24 => Snowflake\Publications\Domain\Model\Publicationprototypepersistent entity (uid=5777, pid=124)
      originalId => protected5777 (integer)
      authors => protected'Winkel,&nbsp;L.; Berg,&nbsp;M.; Amini,&nbsp;M.; Hug,&nbsp;S.&nbsp;J.; Johnso
         n,&nbsp;C.&nbsp;A.' (94 chars)
      title => protected'Predicting groundwater arsenic contamination in Southeast Asia from surface 
         parameters' (86 chars)
      journal => protected'Nature Geoscience' (17 chars)
      year => protected2008 (integer)
      volume => protected1 (integer)
      issue => protected'' (0 chars)
      startpage => protected'536' (3 chars)
      otherpage => protected'542' (3 chars)
      categories => protected'' (0 chars)
      description => protected'Arsenic contamination of groundwater resources threatens the health of milli
         ons of people worldwide, particularly in the densely populated river deltas 
         of Southeast Asia. Although many arsenic-affected areas have been identified
          in recent years, a systematic evaluation of vulnerable areas remains to be 
         carried out. Here we present maps pinpointing areas at risk of groundwater a
         rsenic concentrations exceeding 10 g l<SUP>-1</SUP>. These maps were produce
         d by combining geological and surface soil parameters in a logistic regressi
         on model, calibrated with 1,756 aggregated and geo-referenced groundwater da
         ta points from the Bengal, Red River and Mekong deltas. We show that Holocen
         e deltaic and organic-rich surface sediments are key indicators for arsenic 
         risk areas and that the combination of surface parameters is a successful ap
         proach to predict groundwater arsenic contamination. Predictions are in good
          agreement with the known spatial distribution of arsenic contamination, and
          further indicate elevated risks in Sumatra and Myanmar, where no groundwate
         r studies exist.' (1080 chars)
      serialnumber => protected'1752-0894' (9 chars)
      doi => protected'10.1038/ngeo254' (15 chars)
      uid => protected5777 (integer)
      _localizedUid => protected5777 (integer)modified
      _languageUid => protectedNULL
      _versionedUid => protected5777 (integer)modified
      pid => protected124 (integer)