{"title":"ArcNLET-Py:使用 Python 为 ArcGIS pro 开发的基于 ArcGIS 的氮负荷估算工具箱","authors":"","doi":"10.1016/j.softx.2024.101816","DOIUrl":null,"url":null,"abstract":"<div><p>Onsite Sewage Treatment and Disposal Systems (OSTDS) are privately owned infrastructures that are critical for treating domestic wastewater in the USA. The ArcGIS-based Nitrogen Load Estimation Toolbox (ArcNLET) was developed to estimate nitrogen load from OSTDS to groundwater and surface waterbodies by simulating reactive transport of ammonium and nitrate nitrogen in soils and unconfined groundwater aquifers. Quantifying the load and removal of wastewater effluent requires resources, including data, computational power, and professional expertise that are not always available to state and local government agencies. Here, we discuss the advantages of utilizing a simplified model within a GIS to overcome data restraints, simulate the transport and load of nitrogen, and elaborate on the process of renovating and updating ArcNLET for integration with Python and ArcGIS Pro. ArcNLET-Py has the following modules: Module 0 for pre-processing the SSURGO database, Module 1 for groundwater flow simulation, Module 2 for particle tracking, Module 3 for a Vadose Zone MODel (VZMOD), Module 4 for nitrogen reactive transport modeling, and Module 5 for estimating nitrogen load to groundwater and surface waterbodies. The nitrogen reactions considered in Module 3 and Module 4 include ammonium sorption, ammonium nitrification, and nitrate denitrification in the flow path from OSTDS to surface waterbodies. As a newly introduced module, Module 0 streamlines the preparation of soil data from the SSURGO database, thereby enhancing the ease of use of ArcNLET-Py. An example of using ArcNLET-Py is presented for estimating nitrogen load in the Lakeshore neighborhood of Jacksonville, Florida. This work demonstrates the feasibility of developing complex pollution assessment software in the Python environment of ArcGIS Pro and holds implications for water environment protection.</p></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352711024001870/pdfft?md5=103d04e08eb0d6850cb1fd1e254c9bc6&pid=1-s2.0-S2352711024001870-main.pdf","citationCount":"0","resultStr":"{\"title\":\"ArcNLET-Py: An ArcGIS-based nitrogen load estimation toolbox developed using python for ArcGIS pro\",\"authors\":\"\",\"doi\":\"10.1016/j.softx.2024.101816\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Onsite Sewage Treatment and Disposal Systems (OSTDS) are privately owned infrastructures that are critical for treating domestic wastewater in the USA. The ArcGIS-based Nitrogen Load Estimation Toolbox (ArcNLET) was developed to estimate nitrogen load from OSTDS to groundwater and surface waterbodies by simulating reactive transport of ammonium and nitrate nitrogen in soils and unconfined groundwater aquifers. Quantifying the load and removal of wastewater effluent requires resources, including data, computational power, and professional expertise that are not always available to state and local government agencies. Here, we discuss the advantages of utilizing a simplified model within a GIS to overcome data restraints, simulate the transport and load of nitrogen, and elaborate on the process of renovating and updating ArcNLET for integration with Python and ArcGIS Pro. ArcNLET-Py has the following modules: Module 0 for pre-processing the SSURGO database, Module 1 for groundwater flow simulation, Module 2 for particle tracking, Module 3 for a Vadose Zone MODel (VZMOD), Module 4 for nitrogen reactive transport modeling, and Module 5 for estimating nitrogen load to groundwater and surface waterbodies. The nitrogen reactions considered in Module 3 and Module 4 include ammonium sorption, ammonium nitrification, and nitrate denitrification in the flow path from OSTDS to surface waterbodies. As a newly introduced module, Module 0 streamlines the preparation of soil data from the SSURGO database, thereby enhancing the ease of use of ArcNLET-Py. An example of using ArcNLET-Py is presented for estimating nitrogen load in the Lakeshore neighborhood of Jacksonville, Florida. This work demonstrates the feasibility of developing complex pollution assessment software in the Python environment of ArcGIS Pro and holds implications for water environment protection.</p></div>\",\"PeriodicalId\":21905,\"journal\":{\"name\":\"SoftwareX\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-07-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2352711024001870/pdfft?md5=103d04e08eb0d6850cb1fd1e254c9bc6&pid=1-s2.0-S2352711024001870-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SoftwareX\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352711024001870\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SoftwareX","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352711024001870","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
ArcNLET-Py: An ArcGIS-based nitrogen load estimation toolbox developed using python for ArcGIS pro
Onsite Sewage Treatment and Disposal Systems (OSTDS) are privately owned infrastructures that are critical for treating domestic wastewater in the USA. The ArcGIS-based Nitrogen Load Estimation Toolbox (ArcNLET) was developed to estimate nitrogen load from OSTDS to groundwater and surface waterbodies by simulating reactive transport of ammonium and nitrate nitrogen in soils and unconfined groundwater aquifers. Quantifying the load and removal of wastewater effluent requires resources, including data, computational power, and professional expertise that are not always available to state and local government agencies. Here, we discuss the advantages of utilizing a simplified model within a GIS to overcome data restraints, simulate the transport and load of nitrogen, and elaborate on the process of renovating and updating ArcNLET for integration with Python and ArcGIS Pro. ArcNLET-Py has the following modules: Module 0 for pre-processing the SSURGO database, Module 1 for groundwater flow simulation, Module 2 for particle tracking, Module 3 for a Vadose Zone MODel (VZMOD), Module 4 for nitrogen reactive transport modeling, and Module 5 for estimating nitrogen load to groundwater and surface waterbodies. The nitrogen reactions considered in Module 3 and Module 4 include ammonium sorption, ammonium nitrification, and nitrate denitrification in the flow path from OSTDS to surface waterbodies. As a newly introduced module, Module 0 streamlines the preparation of soil data from the SSURGO database, thereby enhancing the ease of use of ArcNLET-Py. An example of using ArcNLET-Py is presented for estimating nitrogen load in the Lakeshore neighborhood of Jacksonville, Florida. This work demonstrates the feasibility of developing complex pollution assessment software in the Python environment of ArcGIS Pro and holds implications for water environment protection.
期刊介绍:
SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.