The Presence of Denitrifiers In Bacterial Communities of Urban Stormwater Best Management Practices (BMPs)

AbstractStormwater best management practices (BMPs) are engineered structures that attempt to mitigate the impacts of stormwater, which can include nitrogen inputs from the surrounding drainage area. The goal of this study was to assess bacterial community composition in different types of stormwater BMP soils to establish whether a particular BMP type harbors more denitrification potential. Soil sampling took place over the summer of 2015 following precipitation events. Soils were sampled from four bioretention facilities, four dry ponds, four surface sand filters, and one dry swale. 16S rRNA gene analysis of extracted DNA and RNA amplicons indicated high bacterial diversity in the soils of all BMP types sampled. An abundance of denitrifiers was also indicated in the extracted DNA using presence/absence of nirS, nirK, and nosZ denitrification genes. BMP soil bacterial communities were impacted by the surrounding soil physiochemistry. Based on the identification of a metabolically-active community of denitrifiers, this study has indicated that denitrification could potentially occur under appropriate conditions in all types of BMP sampled, including surface sand filters that are often viewed as providing low potential for denitrification. The carbon content of incoming stormwater could be providing bacterial communities with denitrification conditions. The findings of this study are especially relevant for land managers in watersheds with legacy nitrogen from former agricultural land use.

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Implementation of best management practices in agriculture: modelling and monitoring of impacts on nitrogen leaching

Water Science & Technology ◽

10.2166/wst.2002.0201 ◽

2002 ◽

Vol 45 (9) ◽

pp. 43-50 ◽

Cited By ~ 7

Author(s):

A. Joelsson ◽

K. Kyllmar

Keyword(s):

Best Management Practices ◽

Management Practices ◽

Time Perspective ◽

Agricultural Land ◽

Root Zone ◽

Agricultural Practices ◽

Nitrogen Leaching ◽

Best Management ◽

Indexing Technique ◽

The Impact

In Kattegat and the coastal water of the Baltic Sea, high nitrogen input from agricultural land is considered to be the main reason for eutrophication. International agreements and governmental programs have set a target to reduce the anthropogenic nitrogen load by 50 percent. Improved nitrogen removal in treatment plants and efforts in agriculture have so far not decreased nitrogen transport to a sufficient extent. In this project the impact of agricultural practices on nitrogen leaching was investigated in two small agricultural catchments in Southwest Sweden. The root-zone leaching was estimated by an indexing technique. Simultaneously the transports in the stream outlets were monitored. During 1995 and 1999 the agricultural practices in the catchments were surveyed. Field data from the first survey indicated that fertilisation did not always match crop requirements, the area of undersown catch crop can be increased and autumn cultivation can be reduced. The second survey was preceded by an advisory campaign where each farmer was visited and presented with an environmental plan including fertilisation, cultivation, and crop rotation for the farm. The plan summarised the best management practices that could be realised under actual conditions. Results from the second survey showed that some changes in the agricultural practices were carried out after the advisory campaign. The nitrogen leaching from the root-zone was then estimated by the indexing technique, both for the time before and after the advisory program. The results showed that the estimated nitrogen leaching, as a mean value for 330 fields, decreased from 53 to 50 kgN ha−1, due to adjustments of the agricultural practices. Monitoring of stream transports showed values of the same magnitude after correction for retention and other sources. In this short time perspective, decreases in transport due to changes in agricultural practices could not be separated from influence of weather conditions. In comparison to results from the Swedish monitoring programme for agriculture, the measured transports were normal for the region, where annual variation in precipitation and runoff is large. Theoretically, nitrogen leaching could be reduced by one third without any major economic constraints for the farmers. In general, the farmers were positive to advice and willing to try new farming techniques even if some measurements were not fully implemented during the investigation period.

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Comparison of RUSLE and MMF Soil Loss Models and Evaluation of Catchment Scale Best Management Practices for a Mountainous Watershed in India

Sustainability ◽

10.3390/su13010232 ◽

2020 ◽

Vol 13 (1) ◽

pp. 232

Author(s):

Susanta Das ◽

Proloy Deb ◽

Pradip Kumar Bora ◽

Prafull Katre

Keyword(s):

Soil Erosion ◽

Best Management Practices ◽

Soil Loss ◽

Management Practices ◽

Agricultural Land ◽

Soil Layer ◽

Catchment Scale ◽

Suitable Alternative ◽

Best Management ◽

Management Plans

Soil erosion from arable lands removes the top fertile soil layer (comprised of humus/organic matter) and therefore requires fertilizer application which affects the overall sustainability. Hence, determination of soil erosion from arable lands is crucial to planning conservation measures. A modeling approach is a suitable alternative to estimate soil loss in ungauged catchments. Soil erosion primarily depends on soil texture, structure, infiltration, topography, land uses, and other erosive forces like water and wind. By analyzing these parameters, coupled with geospatial tools, models can estimate storm wise and annual average soil losses. In this study, a hilly watershed called Nongpoh was considered with the objective of prioritizing critical erosion hazard areas within the micro-catchment based on average annual soil loss and land use and land cover and making appropriate management plans for the prioritized areas. Two soil erosion models namely Revised Universal Soil Loss Equation (RUSLE) and Modified Morgan–Morgan–Finney (MMF) models were used to estimate soil loss with the input parameters extracted from satellite information and automatic weather stations. The RUSLE and MMF models showed similar results in estimating soil loss, except the MMF model estimated 7.74% less soil loss than the RUSLE model from the watershed. The results also indicated that the study area is under severe erosion class, whereas agricultural land, open forest area, and scrubland were prioritized most erosion prone areas within the watershed. Based on prioritization, best management plans were developed at catchment scale for reducing soil loss. These findings and the methodology employed can be widely used in mountainous to hilly watersheds around the world for identifying best management practices (BMP).

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Assessing the Efficiency of Alternative Best Management Practices to Reduce Nonpoint Source Pollution in a Rural Watershed Located in Louisiana, USA

Water ◽

10.3390/w11081714 ◽

2019 ◽

Vol 11 (8) ◽

pp. 1714 ◽

Cited By ~ 3

Author(s):

Bijay K. Pokhrel ◽

Krishna P. Paudel

Keyword(s):

Best Management Practices ◽

Management Practices ◽

Nutrient Management ◽

Agricultural Land ◽

Programming Model ◽

Cost Minimization ◽

Poultry Production ◽

Linear Regression Models ◽

Best Management ◽

Phosphorus Runoff

We conducted biophysical simulations using MapShed to determine the effects of adopting best management practices (BMPs) to reduce nutrients and sediment pollution in a watershed dominated by poultry production in the Saline Bayou Watershed, Louisiana, USA. The reduction of three water pollutants, nitrogen, phosphorus, and sediment from adopting different BMPs were assessed using a linear programming model with the cost minimization objective. We considered three weather scenarios (dry, normal, and wet) and BMP parameter efficiencies obtained from linear regression models. Optimization results showed that nutrient management and agricultural land retirement reduced most of the phosphorus runoff in the watershed at the lowest cost. Results were robust to alternative weather (dry, normal, and wet) scenarios.

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