Identification of critical source areas under present and projected land use for effective management of diffuse pollutants in an urbanized watershed

Bello, Al-Amin Danladi; Haniffah, Mohd Ridza Mohd; Hanapi, Muhammad Nassir; Usman, Aliyu Bamaiyi

doi:10.1080/15715124.2018.1461108

Cited by 10 publications

(4 citation statements)

References 45 publications

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“…The LULC is directly related to NPS pollution and affects the composition of CSAs (Bello et al 2018). Here, the CSAs were generally distributed in cultivated land (73.22%) and where the largest populations among rural settlements (289 villages) occurred in the study area.…”

Section: Identification Of Csas With Multi-factorsmentioning

confidence: 88%

“…According to the HWSD soil classification, Calcaric Cambisols and Calcaric Fluvisols are the main soil types in CSAs, whose soil texture is largely loam. The premie factors relevant to soil in CSAs are land use changes and pollutants' composition (Bello et al 2018). High nutrient application rates increase nutrient availability and fosters nutrients' accumulation in soil.…”

Section: Identification Of Csas With Multi-factorsmentioning

confidence: 99%

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Identifying critical source areas of nonpoint source pollution in a watershed with SWAT–ECM and AHP methods

2021

Hydrology Research

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Identification of critical source areas (CSAs) is pivotal for the management of nonpoint source (NPS) pollution of watersheds. Most studies focus on source (S) factors and ignore the driving (D) factors of such pollution. The Soil and Water Assessment Tool (SWAT) model and the export coefficient method (ECM) were incorporated to quantify the S factors of ammonia nitrogen (NH4–N) and total phosphorus (TP) as NPS pollution. Specifically, S factors coupled with D factors, including precipitation, slope, soil and land use, were regarded as multi–factors. Moreover, the analytical hierarchy process (AHP) method was adopted to determine the respective weights of multi–factors after overlaying the factor maps to identify the CSAs. These CSAs accounted for 23.86% of the total area, and generated 54.94% of NH4–N and 42.59% of the TP loads. In contrast with single and multi–factors, we found that using multi–factors having differing weights was more accurate for identifying CSAs. Our study results indicate this approach is reasonable for CSAs' identification in watersheds, and it can provide insights into different pollution sources and migration, thus providing a sounder basis for future decision–making.

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Section: Identification Of Csas With Multi-factorsmentioning

confidence: 88%

Section: Identification Of Csas With Multi-factorsmentioning

confidence: 99%

Identifying critical source areas of nonpoint source pollution in a watershed with SWAT–ECM and AHP methods

2021

Hydrology Research

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“…The Skudai river catchment in southern Malaysia (total drainage area 288 km 2 ; 30 see Supporting Information (SI) Figure S1) is composed of urban/developed, agricultural (80% oil palm, 20% rubber plantations), and forest land in roughly equal proportions. 31 The Skudai catchment lays within the Johor Bahru district (1865 km 2 with 1.4 million inhabitants 32 ). Similar to many LMIC settings, sewage treatment in the Skudai catchment is inconsistent, sometimes with poorly defined discharge locations.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

Developing Surrogate Markers for Predicting Antibiotic Resistance “Hot Spots” in Rivers Where Limited Data Are Available

Ott

O’Donnell

Trần

et al. 2021

Environ. Sci. Technol.

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Pinpointing environmental antibiotic resistance (AR) hot spots in low-and middle-income countries (LMICs) is hindered by a lack of available and comparable AR monitoring data relevant to such settings. Addressing this problem, we performed a comprehensive spatial and seasonal assessment of water quality and AR conditions in a Malaysian river catchment to identify potential "simple" surrogates that mirror elevated AR. We screened for resistant coliforms, 22 antibiotics, 287 AR genes and integrons, and routine water quality parameters, covering absolute concentrations and mass loadings. To understand relationships, we introduced standardized "effect sizes" (Cohen's D) for AR monitoring to improve comparability of field studies. Overall, water quality generally declined and environmental AR levels increased as one moved down the catchment without major seasonal variations, except total antibiotic concentrations that were higher in the dry season (Cohen's D > 0.8, P < 0.05). Among simple surrogates, dissolved oxygen (DO) most strongly correlated (inversely) with total AR gene concentrations (Spearman's ρ 0.81, P < 0.05). We suspect this results from minimally treated sewage inputs, which also contain AR bacteria and genes, depleting DO in the most impacted reaches. Thus, although DO is not a measure of AR, lower DO levels reflect wastewater inputs, flagging possible AR hot spots. DO measurement is inexpensive, already monitored in many catchments, and exists in many numerical water quality models (e.g., oxygen sag curves). Therefore, we propose combining DO data and prospective modeling to guide local interventions, especially in LMIC rivers with limited data.

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“…The HSPF model was used for predicting flows of a watershed that joins modeling of watersheds and streams [39][40][41]. In addition, the HSPF model can be used to incorporate the transport of pollutants and nutrients.…”

Section: Hspf Model Descriptionmentioning

confidence: 99%

Net Ecosystem Production of a River Relying on Hydrology, Hydrodynamics and Water Quality Monitoring Stations

Rojano

Huber

Ugwuanyi

et al. 2020

Water

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Flow and water quality of rivers are highly dynamic. Water quantity and quality are subjected to simultaneous physical, chemical and biological processes making it difficult to accurately assess lotic ecosystems. Our study investigated net ecosystem production (NEP) relying on high-frequency data of hydrology, hydrodynamics and water quality. The Kanawha River, West Virginia was investigated along 52.8 km to estimate NEP. Water quality data were collected along the river using three distributed multiprobe sondes that measured water temperature, dissolved oxygen, dissolved oxygen saturation, specific conductance, turbidity and ORP hourly for 71 days. Flows along the river were predicted by means of the hydrologic and hydrodynamic models in Hydrologic Simulation Program in Fortran (HSPF). It was found that urban local inflows were correlated with NEP. However, under hypoxic conditions, local inflows were correlated with specific conductance. Thus, our approach represents an effort for the systematic integration of data derived from models and field measurements with the aim of providing an improved assessment of lotic ecosystems.

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Identification of critical source areas under present and projected land use for effective management of diffuse pollutants in an urbanized watershed

Cited by 10 publications

References 45 publications

Identifying critical source areas of nonpoint source pollution in a watershed with SWAT–ECM and AHP methods

Identifying critical source areas of nonpoint source pollution in a watershed with SWAT–ECM and AHP methods

Developing Surrogate Markers for Predicting Antibiotic Resistance “Hot Spots” in Rivers Where Limited Data Are Available

Net Ecosystem Production of a River Relying on Hydrology, Hydrodynamics and Water Quality Monitoring Stations

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