{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T19:35:57Z","timestamp":1770060957055,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,2,25]],"date-time":"2024-02-25T00:00:00Z","timestamp":1708819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT\u2013Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","award":["UIDB\/00006\/2020"],"award-info":[{"award-number":["UIDB\/00006\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMSE"],"abstract":"<jats:p>Predicting the levels of a pollutant in a given area is an open problem, mainly because historical data are typically available at certain locations, where monitoring stations are located, but not at all locations in the area. This work presents an approach based on developing predictions at each of the points where an immission station is available; in this case, based on shallow Artificial Neural Networks, ANNs, and then using a simple geostatistical interpolation algorithm (Inverse Distance Weighted, IDW), a pollutant map is constructed over the entire study area, thus providing predictions at each point in the plane. The ANN models are designed to make 1 h ahead and 4 h ahead predictions, using an autoregressive scheme as inputs (in the case of 4 h ahead as a jumping strategy). The results are then compared using the Friedman and Bonferroni tests to select the best model at each location, and predictions are made with all the best models. In general, to the 1 h ahead prediction models, the optimal models typically have fewer neurons and require minimal historical data. For instance, the best model in Algeciras has an R of almost 0.89 and consists of 1 hidden neuron and 3 to 5 lags, similar to Colegio Los Barrios. In the case of 4h ahead prediction, Colegio Carteya station shows the best model, with an R of almost 0.89 and a MSE of less than 240, including 5 hidden neurons and different lags from the past. The results are sufficiently adequate, especially in the case of predictions 4 h into the future. The aim is to integrate the models into a tool for citizens and administrations to make decisions.<\/jats:p>","DOI":"10.3390\/jmse12030397","type":"journal-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T07:51:39Z","timestamp":1708933899000},"page":"397","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Air Pollution PM10 Forecasting Maps in the Maritime Area of the Bay of Algeciras (Spain)"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1364-1678","authenticated-orcid":false,"given":"Mar\u00eda Inmaculada","family":"Rodr\u00edguez-Garc\u00eda","sequence":"first","affiliation":[{"name":"Department of Computer Science Engineering, Algeciras School of Engineering and Technology (ASET), University of C\u00e1diz, 11002 Algeciras, Cadiz, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8478-6431","authenticated-orcid":false,"given":"Mar\u00eda Gema","family":"Carrasco-Garc\u00eda","sequence":"additional","affiliation":[{"name":"Department of Industrial and Civil Engineering, Algeciras School of Engineering and Technology (ASET), University of C\u00e1diz, 11002 Algeciras, Cadiz, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0185-3200","authenticated-orcid":false,"given":"Maria da Concei\u00e7\u00e3o Rodrigues","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"Engineering Institute, University of Algarve, Campus da Penha, 8005-139 Faro, Portugal"},{"name":"CEAUL\u2014Centre de Estat\u00edstica e Aplica\u00e7\u00f5es da Universidade de Lisboa, Faculdade de Ci\u00eancias, Universidade de Lisboa, 1749-016 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5765-369X","authenticated-orcid":false,"given":"Javier","family":"Gonz\u00e1lez-Enrique","sequence":"additional","affiliation":[{"name":"Department of Computer Science Engineering, Algeciras School of Engineering and Technology (ASET), University of C\u00e1diz, 11002 Algeciras, Cadiz, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2170-0693","authenticated-orcid":false,"given":"Juan Jes\u00fas","family":"Ruiz-Aguilar","sequence":"additional","affiliation":[{"name":"Department of Industrial and Civil Engineering, Algeciras School of Engineering and Technology (ASET), University of C\u00e1diz, 11002 Algeciras, Cadiz, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4627-0252","authenticated-orcid":false,"given":"Ignacio J.","family":"Turias","sequence":"additional","affiliation":[{"name":"Department of Computer Science Engineering, Algeciras School of Engineering and Technology (ASET), University of C\u00e1diz, 11002 Algeciras, Cadiz, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"110791","DOI":"10.1016\/j.jenvman.2020.110791","article-title":"Air pollution characteristics and human health risks in key citied of northwest China","volume":"296","author":"Luo","year":"2020","journal-title":"J. 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