Agent-based modelling of interactions between air pollutants and greenery using a case study of Yerevan, Armenia
Urban greenery such as trees can effectively reduce air pollution in a natural and eco-friendly way. However, how to spatially locate and arrange greenery in an optimal way remains as a challenging task. We developed an agent-based model of air pollution dynamics to support the optimal allocation an...
Gespeichert in:
| Veröffentlicht in: | Environmental modelling & software : with environment data news 2019-06, Vol.116, p.7-25 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 25 |
|---|---|
| container_issue | |
| container_start_page | 7 |
| container_title | Environmental modelling & software : with environment data news |
| container_volume | 116 |
| creator | Akopov, Andranik S. Beklaryan, Levon A. Saghatelyan, Armen K. |
| description | Urban greenery such as trees can effectively reduce air pollution in a natural and eco-friendly way. However, how to spatially locate and arrange greenery in an optimal way remains as a challenging task. We developed an agent-based model of air pollution dynamics to support the optimal allocation and configuration of tree clusters in a city. The Pareto optimal solutions for greenery in the city were computed using the suggested heuristic optimisation algorithm, considering the complex absorptive-diffusive interactions between agent-trees (tree clusters) and air pollutants produced by agent-enterprises (factories) and agent-vehicles (car clusters) located in the city. We applied and tested the model with empirical data in Yerevan, Armenia, and successfully found the optimal strategy under the budget constraint: planting various types of trees around kindergartens and emission sources.
•We developed an original agent-based model of the interactions between air pollutants and greenery.•A decision-making system for greenery planning was developed for Yerevan, Armenia.•Two bi-objective optimisation problems were suggested and solved for reducing the air pollution concentration.•The Pareto optimal solutions for urban greenery were computed through a genetic optimisation algorithm. |
| doi_str_mv | 10.1016/j.envsoft.2019.02.003 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2216907510</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S1364815218300902</els_id><sourcerecordid>2216907510</sourcerecordid><originalsourceid>FETCH-LOGICAL-c337t-a3613942bf3594f1724fcd23e0d6c3ecb5a121935ac32b1dabeb03b91dc91dd53</originalsourceid><addsrcrecordid>eNqFkE1LxDAQhoMouK7-BCHg1dZ89GN7kkX8ggUvevAU0mS6pHSTmqQr--9N2b17GGZg3vcZ5kXolpKcElo99DnYfXBdzBmhTU5YTgg_Qwu6qnlW1aw6TzOvimxFS3aJrkLoCSFpLhZoXG_BxqyVATTeOQ3DYOwWuw4bG8FLFY2zAbcQfwEslsbj0Q3DFKWNAUur8danBfgDnsLslFglFg5x0ocZ8w0e9tLe47XfgTXyGl10cghwc-pL9PXy_Pn0lm0-Xt-f1ptMcV7HTPKK8qZgbcfLpuhozYpOacaB6EpxUG0pKaMNL6XirKVattAS3jZUq1S65Et0d-SO3v1MEKLo3eRtOikYo1VD6pKSpCqPKuVdCB46MXqzk_4gKBFzuKIXp3DFHK4gTKRwk-_x6IP0wt6AF0EZsAq08aCi0M78Q_gDteiHXA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2216907510</pqid></control><display><type>article</type><title>Agent-based modelling of interactions between air pollutants and greenery using a case study of Yerevan, Armenia</title><source>Elsevier ScienceDirect Journals</source><creator>Akopov, Andranik S. ; Beklaryan, Levon A. ; Saghatelyan, Armen K.</creator><creatorcontrib>Akopov, Andranik S. ; Beklaryan, Levon A. ; Saghatelyan, Armen K.</creatorcontrib><description>Urban greenery such as trees can effectively reduce air pollution in a natural and eco-friendly way. However, how to spatially locate and arrange greenery in an optimal way remains as a challenging task. We developed an agent-based model of air pollution dynamics to support the optimal allocation and configuration of tree clusters in a city. The Pareto optimal solutions for greenery in the city were computed using the suggested heuristic optimisation algorithm, considering the complex absorptive-diffusive interactions between agent-trees (tree clusters) and air pollutants produced by agent-enterprises (factories) and agent-vehicles (car clusters) located in the city. We applied and tested the model with empirical data in Yerevan, Armenia, and successfully found the optimal strategy under the budget constraint: planting various types of trees around kindergartens and emission sources.
•We developed an original agent-based model of the interactions between air pollutants and greenery.•A decision-making system for greenery planning was developed for Yerevan, Armenia.•Two bi-objective optimisation problems were suggested and solved for reducing the air pollution concentration.•The Pareto optimal solutions for urban greenery were computed through a genetic optimisation algorithm.</description><identifier>ISSN: 1364-8152</identifier><identifier>EISSN: 1873-6726</identifier><identifier>DOI: 10.1016/j.envsoft.2019.02.003</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Absorptivity ; Agent-based modelling ; Agent-based models ; Air pollution ; Air pollution dynamics ; Algorithms ; Armenia ; Automobiles ; Case studies ; Clusters ; Evolutionary algorithms ; Greenery ; Industrial plants ; Kindergarten ; Model testing ; Optimization ; Pollutants ; Pollution ; Pollution control ; Pollution effects ; Pollution sources ; Simulation of complex environmental systems ; Trees</subject><ispartof>Environmental modelling & software : with environment data news, 2019-06, Vol.116, p.7-25</ispartof><rights>2019 Elsevier Ltd</rights><rights>Copyright Elsevier Science Ltd. Jun 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c337t-a3613942bf3594f1724fcd23e0d6c3ecb5a121935ac32b1dabeb03b91dc91dd53</citedby><cites>FETCH-LOGICAL-c337t-a3613942bf3594f1724fcd23e0d6c3ecb5a121935ac32b1dabeb03b91dc91dd53</cites><orcidid>0000-0003-0627-3037</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S1364815218300902$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids></links><search><creatorcontrib>Akopov, Andranik S.</creatorcontrib><creatorcontrib>Beklaryan, Levon A.</creatorcontrib><creatorcontrib>Saghatelyan, Armen K.</creatorcontrib><title>Agent-based modelling of interactions between air pollutants and greenery using a case study of Yerevan, Armenia</title><title>Environmental modelling & software : with environment data news</title><description>Urban greenery such as trees can effectively reduce air pollution in a natural and eco-friendly way. However, how to spatially locate and arrange greenery in an optimal way remains as a challenging task. We developed an agent-based model of air pollution dynamics to support the optimal allocation and configuration of tree clusters in a city. The Pareto optimal solutions for greenery in the city were computed using the suggested heuristic optimisation algorithm, considering the complex absorptive-diffusive interactions between agent-trees (tree clusters) and air pollutants produced by agent-enterprises (factories) and agent-vehicles (car clusters) located in the city. We applied and tested the model with empirical data in Yerevan, Armenia, and successfully found the optimal strategy under the budget constraint: planting various types of trees around kindergartens and emission sources.
•We developed an original agent-based model of the interactions between air pollutants and greenery.•A decision-making system for greenery planning was developed for Yerevan, Armenia.•Two bi-objective optimisation problems were suggested and solved for reducing the air pollution concentration.•The Pareto optimal solutions for urban greenery were computed through a genetic optimisation algorithm.</description><subject>Absorptivity</subject><subject>Agent-based modelling</subject><subject>Agent-based models</subject><subject>Air pollution</subject><subject>Air pollution dynamics</subject><subject>Algorithms</subject><subject>Armenia</subject><subject>Automobiles</subject><subject>Case studies</subject><subject>Clusters</subject><subject>Evolutionary algorithms</subject><subject>Greenery</subject><subject>Industrial plants</subject><subject>Kindergarten</subject><subject>Model testing</subject><subject>Optimization</subject><subject>Pollutants</subject><subject>Pollution</subject><subject>Pollution control</subject><subject>Pollution effects</subject><subject>Pollution sources</subject><subject>Simulation of complex environmental systems</subject><subject>Trees</subject><issn>1364-8152</issn><issn>1873-6726</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNqFkE1LxDAQhoMouK7-BCHg1dZ89GN7kkX8ggUvevAU0mS6pHSTmqQr--9N2b17GGZg3vcZ5kXolpKcElo99DnYfXBdzBmhTU5YTgg_Qwu6qnlW1aw6TzOvimxFS3aJrkLoCSFpLhZoXG_BxqyVATTeOQ3DYOwWuw4bG8FLFY2zAbcQfwEslsbj0Q3DFKWNAUur8danBfgDnsLslFglFg5x0ocZ8w0e9tLe47XfgTXyGl10cghwc-pL9PXy_Pn0lm0-Xt-f1ptMcV7HTPKK8qZgbcfLpuhozYpOacaB6EpxUG0pKaMNL6XirKVattAS3jZUq1S65Et0d-SO3v1MEKLo3eRtOikYo1VD6pKSpCqPKuVdCB46MXqzk_4gKBFzuKIXp3DFHK4gTKRwk-_x6IP0wt6AF0EZsAq08aCi0M78Q_gDteiHXA</recordid><startdate>201906</startdate><enddate>201906</enddate><creator>Akopov, Andranik S.</creator><creator>Beklaryan, Levon A.</creator><creator>Saghatelyan, Armen K.</creator><general>Elsevier Ltd</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7SC</scope><scope>7ST</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0003-0627-3037</orcidid></search><sort><creationdate>201906</creationdate><title>Agent-based modelling of interactions between air pollutants and greenery using a case study of Yerevan, Armenia</title><author>Akopov, Andranik S. ; Beklaryan, Levon A. ; Saghatelyan, Armen K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c337t-a3613942bf3594f1724fcd23e0d6c3ecb5a121935ac32b1dabeb03b91dc91dd53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Absorptivity</topic><topic>Agent-based modelling</topic><topic>Agent-based models</topic><topic>Air pollution</topic><topic>Air pollution dynamics</topic><topic>Algorithms</topic><topic>Armenia</topic><topic>Automobiles</topic><topic>Case studies</topic><topic>Clusters</topic><topic>Evolutionary algorithms</topic><topic>Greenery</topic><topic>Industrial plants</topic><topic>Kindergarten</topic><topic>Model testing</topic><topic>Optimization</topic><topic>Pollutants</topic><topic>Pollution</topic><topic>Pollution control</topic><topic>Pollution effects</topic><topic>Pollution sources</topic><topic>Simulation of complex environmental systems</topic><topic>Trees</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Akopov, Andranik S.</creatorcontrib><creatorcontrib>Beklaryan, Levon A.</creatorcontrib><creatorcontrib>Saghatelyan, Armen K.</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Computer and Information Systems Abstracts</collection><collection>Environment Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Environment Abstracts</collection><jtitle>Environmental modelling & software : with environment data news</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Akopov, Andranik S.</au><au>Beklaryan, Levon A.</au><au>Saghatelyan, Armen K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Agent-based modelling of interactions between air pollutants and greenery using a case study of Yerevan, Armenia</atitle><jtitle>Environmental modelling & software : with environment data news</jtitle><date>2019-06</date><risdate>2019</risdate><volume>116</volume><spage>7</spage><epage>25</epage><pages>7-25</pages><issn>1364-8152</issn><eissn>1873-6726</eissn><abstract>Urban greenery such as trees can effectively reduce air pollution in a natural and eco-friendly way. However, how to spatially locate and arrange greenery in an optimal way remains as a challenging task. We developed an agent-based model of air pollution dynamics to support the optimal allocation and configuration of tree clusters in a city. The Pareto optimal solutions for greenery in the city were computed using the suggested heuristic optimisation algorithm, considering the complex absorptive-diffusive interactions between agent-trees (tree clusters) and air pollutants produced by agent-enterprises (factories) and agent-vehicles (car clusters) located in the city. We applied and tested the model with empirical data in Yerevan, Armenia, and successfully found the optimal strategy under the budget constraint: planting various types of trees around kindergartens and emission sources.
•We developed an original agent-based model of the interactions between air pollutants and greenery.•A decision-making system for greenery planning was developed for Yerevan, Armenia.•Two bi-objective optimisation problems were suggested and solved for reducing the air pollution concentration.•The Pareto optimal solutions for urban greenery were computed through a genetic optimisation algorithm.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.envsoft.2019.02.003</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0003-0627-3037</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1364-8152 |
| ispartof | Environmental modelling & software : with environment data news, 2019-06, Vol.116, p.7-25 |
| issn | 1364-8152 1873-6726 |
| language | eng |
| recordid | cdi_proquest_journals_2216907510 |
| source | Elsevier ScienceDirect Journals |
| subjects | Absorptivity Agent-based modelling Agent-based models Air pollution Air pollution dynamics Algorithms Armenia Automobiles Case studies Clusters Evolutionary algorithms Greenery Industrial plants Kindergarten Model testing Optimization Pollutants Pollution Pollution control Pollution effects Pollution sources Simulation of complex environmental systems Trees |
| title | Agent-based modelling of interactions between air pollutants and greenery using a case study of Yerevan, Armenia |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-11T03%3A34%3A23IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Agent-based%20modelling%20of%20interactions%20between%20air%20pollutants%20and%20greenery%20using%20a%20case%20study%20of%20Yerevan,%20Armenia&rft.jtitle=Environmental%20modelling%20&%20software%20:%20with%20environment%20data%20news&rft.au=Akopov,%20Andranik%20S.&rft.date=2019-06&rft.volume=116&rft.spage=7&rft.epage=25&rft.pages=7-25&rft.issn=1364-8152&rft.eissn=1873-6726&rft_id=info:doi/10.1016/j.envsoft.2019.02.003&rft_dat=%3Cproquest_cross%3E2216907510%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2216907510&rft_id=info:pmid/&rft_els_id=S1364815218300902&rfr_iscdi=true |