{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T21:52:11Z","timestamp":1774561931349,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,7,21]],"date-time":"2019-07-21T00:00:00Z","timestamp":1563667200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Environments"],"abstract":"<jats:p>The monitoring of air pollutant concentration within cities is crucial for environment management and public health policies in order to promote sustainable cities. In this study, we present an approach to estimate the concentration of particulate matter of less than 10 \u00b5m diameter (PM10) using an empirical land use regression (LUR) model and considering different remote sensing data as the input. The study area is Quito, the capital of Ecuador, and the data were collected between 2013 and 2017. The model predictors are the surface reflectance bands (visible and infrared) of Landsat-7 ETM+, Landsat-8 OLI\/TIRS, and Aqua-Terra\/MODIS sensors and some environmental indexes (normalized difference vegetation index\u2014NDVI; normalized difference soil index\u2014NDSI, soil-adjusted vegetation index\u2014SAVI; normalized difference water index\u2014NDWI; and land surface temperature (LST)). The dependent variable is PM10 ground measurements. Furthermore, this study also aims to compare three different sources of remote sensing data (Landsat-7 ETM+, Landsat-8 OLI, and Aqua-Terra\/MODIS) to estimate the PM10 concentration, and three different predictive techniques (stepwise regression, partial least square regression, and artificial neuronal network (ANN)) to build the model. The models obtained are able to estimate PM10 in regions where air data acquisition is limited or even does not exist. The best model is the one built with an ANN, where the coefficient of determination (R2 = 0.68) is the highest and the root-mean-square error (RMSE = 6.22) is the lowest among all the models. Thus, the selected model allows the generation of PM10 concentration maps from public remote sensing data, constituting an alternative over other techniques to estimate pollutants, especially when few air quality ground stations are available.<\/jats:p>","DOI":"10.3390\/environments6070085","type":"journal-article","created":{"date-parts":[[2019,7,22]],"date-time":"2019-07-22T03:14:54Z","timestamp":1563765294000},"page":"85","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Assessment of Remote Sensing Data to Model PM10 Estimation in Cities with a Low Number of Air Quality Stations: A Case of Study in Quito, Ecuador"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5629-0893","authenticated-orcid":false,"given":"Cesar I.","family":"Alvarez-Mendoza","sequence":"first","affiliation":[{"name":"Department of Geosciencies, Environment and Land Planning, Faculty of Sciences, University of Porto, Rua Campo Alegre 687, Porto 4169-007, Portugal"},{"name":"Grupo de Investigaci\u00f3n Ambiental en el Desarrollo Sustentable GIADES, Carrera de Ingenier\u00eda Ambiental, Universidad Polit\u00e9cnica Salesiana, Quito 170702, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8043-6431","authenticated-orcid":false,"given":"Ana Claudia","family":"Teodoro","sequence":"additional","affiliation":[{"name":"Department of Geosciencies, Environment and Land Planning, Faculty of Sciences, University of Porto, Rua Campo Alegre 687, Porto 4169-007, Portugal"},{"name":"Earth Sciences Institute (ICT), Pole of the FCUP, University of Porto, Porto 4169-007, Portugal"}]},{"given":"Nelly","family":"Torres","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n Ambiental en el Desarrollo Sustentable GIADES, Carrera de Ingenier\u00eda Ambiental, Universidad Polit\u00e9cnica Salesiana, Quito 170702, Ecuador"}]},{"given":"Valeria","family":"Vivanco","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n Ambiental en el Desarrollo Sustentable GIADES, Carrera de Ingenier\u00eda Ambiental, Universidad Polit\u00e9cnica Salesiana, Quito 170702, Ecuador"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,21]]},"reference":[{"key":"ref_1","unstructured":"(2018, August 30). WHO Ambient (Outdoor) Air Quality and Health. Available online: http:\/\/www.who.int\/news-room\/fact-sheets\/detail\/ambient-(outdoor)-air-quality-and-health."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1007\/s00038-017-0952-y","article-title":"Time to harmonize national ambient air quality standards","volume":"62","author":"Eeftens","year":"2017","journal-title":"Int. J. Public Health"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1007\/s10661-018-6797-x","article-title":"Characteristics of air quality and sources affecting high levels of PM10 and PM2.5 in Poland, Upper Silesia urban area","volume":"190","author":"Kobza","year":"2018","journal-title":"Environ. Monit. Assess."},{"key":"ref_4","unstructured":"World Health Organization Regional Office for Europe (2013). Health Effects of Particulate Matter, World Health Organization Regional Office for Europe."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1186\/1752-153X-8-14","article-title":"PM10 and gaseous pollutants trends from air quality monitoring networks in Bari province: Principal component analysis and absolute principal component scores on a two years and half data set","volume":"8","author":"Ielpo","year":"2014","journal-title":"Chem. Cent. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1080\/10962247.2014.888378","article-title":"A multi-objective assessment of an air quality monitoring network using environmental, economic, and social indicators and GIS-based models","volume":"64","author":"Pope","year":"2014","journal-title":"J. Air Waste Manag. Assoc."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Capezzuto, L., Abbamonte, L., De Vito, S., Massera, E., Formisano, F., Fattoruso, G., Di Francia, G., and Buonanno, A. (2014, January 2\u20135). A maker friendly mobile and social sensing approach to urban air quality monitoring. Proceedings of the IEEE SENSORS 2014, Valencia, Spain.","DOI":"10.1109\/ICSENS.2014.6984920"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hasenfratz, D., Saukh, O., Walser, C., Hueglin, C., Fierz, M., and Thiele, L. (2014, January 24\u201328). Pushing the spatio-temporal resolution limit of urban air pollution maps. Proceedings of the 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom), Budapest, Hungary.","DOI":"10.1109\/PerCom.2014.6813946"},{"key":"ref_9","unstructured":"Alvarez, C.I., Padilla Almeida, O., \u00c1lvarez Mendoza, C.I., and Padilla Almeida, O. (2016). Estimaci\u00f3n de la contaminaci\u00f3n del aire por PM10 en Quito a trav\u00e9s de \u00edndices ambientales con im\u00e1genes LANDSAT ETM+. Rev. Cart, 135\u2013147."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1177\/1753425917699864","article-title":"Particulate matter air pollution from the city of Quito, Ecuador, activates inflammatory signaling pathways in vitro","volume":"23","author":"Cevallos","year":"2017","journal-title":"Innate Immun."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1016\/j.envpol.2016.04.085","article-title":"Assessment of indoor and outdoor PM species at schools and residences in a high-altitude Ecuadorian urban center","volume":"214","author":"Raysoni","year":"2016","journal-title":"Environ. Pollut."},{"key":"ref_12","unstructured":"Alvarez-Mendoza, C.I., Teodoro, A., Torres, N., Vivanco, V., and Ramirez-Cando, L. (2018, January 9). Comparison of satellite remote sensing data in the retrieve of PM10 air pollutant over Quito, Ecuador. Proceedings of the SPIE - The International Society for Optical Engineering, Berlin, Germany."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.envpol.2017.03.079","article-title":"Development of PM2.5 and NO2 models in a LUR framework incorporating satellite remote sensing and air quality model data in Pearl River Delta region, China","volume":"226","author":"Yang","year":"2017","journal-title":"Environ. Pollut."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.envint.2016.11.024","article-title":"Estimation of daily PM10 concentrations in Italy (2006\u20132012) using finely resolved satellite data, land use variables and meteorology","volume":"99","author":"Stafoggia","year":"2017","journal-title":"Environ. Int."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.envres.2017.05.007","article-title":"Incorporating wind availability into land use regression modelling of air quality in mountainous high-density urban environment","volume":"157","author":"Shi","year":"2017","journal-title":"Environ. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.scitotenv.2018.05.144","article-title":"Land use regression models to assess air pollution exposure in Mexico City using finer spatial and temporal input parameters","volume":"639","author":"Son","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zou, B., Chen, J., Zhai, L., Fang, X., Zheng, Z., Zou, B., Chen, J., Zhai, L., Fang, X., and Zheng, Z. (2016). Satellite Based Mapping of Ground PM2.5 Concentration Using Generalized Additive Modeling. Remote Sens., 9.","DOI":"10.3390\/rs9010001"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.envpol.2017.01.074","article-title":"Land-use regression with long-term satellite-based greenness index and culture-specific sources to model PM2.5 spatial-temporal variability","volume":"224","author":"Wu","year":"2017","journal-title":"Environ. Pollut."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.3390\/rs6021587","article-title":"Aerosol indices derived from MODIS data for indicating aerosol-induced air pollution","volume":"6","author":"He","year":"2014","journal-title":"Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Just, A., De Carli, M., Shtein, A., Dorman, M., Lyapustin, A., Kloog, I., Just, A.C., De Carli, M.M., Shtein, A., and Dorman, M. (2018). Correcting Measurement Error in Satellite Aerosol Optical Depth with Machine Learning for Modeling PM2.5 in the Northeastern USA. Remote Sens., 10.","DOI":"10.3390\/rs10050803"},{"key":"ref_21","unstructured":"Wan, Z. (2006). MODIS Land Surface Temperature Products Users\u2019 Guide, Institute for Computational Earth System Science, University of California."},{"key":"ref_22","unstructured":"U.S. Geological Survey (2015). Landsat\u2014Earth Observation Satellites."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.rse.2016.01.007","article-title":"Comparison of Landsat 8 and Landsat 7 for regional measurements of CDOM and water clarity in lakes","volume":"185","author":"Olmanson","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.rse.2013.04.014","article-title":"A Simplified high resolution MODIS aerosol retrieval algorithm (SARA) for use over mixed surfaces","volume":"136","author":"Bilal","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.envpol.2015.09.042","article-title":"Estimating ground-level PM10 in a Chinese city by combining satellite data, meteorological information and a land use regression model","volume":"208","author":"Meng","year":"2016","journal-title":"Environ. Pollut."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Shahraiyni, H.T., and Sodoudi, S. (2016). Statistical modeling approaches for pm10prediction in urban areas; A review of 21st-century studies. Atmosphere, 7.","DOI":"10.3390\/atmos7020015"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2016.04.008","article-title":"Preliminary analysis of the performance of the Landsat 8\/OLI land surface reflectance product","volume":"185","author":"Vermote","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.apgeog.2013.11.011","article-title":"An investigation of the environmental determinants of asthma hospitalizations: An applied spatial approach","volume":"47","author":"Teodoro","year":"2014","journal-title":"Appl. Geogr."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1324","DOI":"10.1016\/j.scitotenv.2018.02.317","article-title":"A land use regression model for explaining spatial variation in air pollution levels using a wind sector based approach","volume":"630","author":"Naughton","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6221","DOI":"10.3390\/rs6076221","article-title":"Exploring the Best Hyperspectral Features for LAI Estimation Using Partial Least Squares Regression","volume":"6","author":"Li","year":"2014","journal-title":"Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Weng, Q. (2016). Remote Sensing of Forest Damage by Diseases and Insects. Remote Sensing for Sustainability, CRC Press.","DOI":"10.1201\/9781315371931"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1038\/jes.2016.9","article-title":"Use of mobile and passive badge air monitoring data for NO X and ozone air pollution spatial exposure prediction models","volume":"27","author":"Xu","year":"2017","journal-title":"J. Expo. Sci. Environ. Epidemiol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JRS.12.042803","article-title":"Modeling soil organic matter and texture from satellite data in areas affected by wildfires and cropland abandonment in Arag\u00f3n, Northern Spain","volume":"12","author":"Vlassova","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s10661-019-7286-6","article-title":"Spatial estimation of surface ozone concentrations in Quito Ecuador with remote sensing data, air pollution measurements and meteorological variables","volume":"191","author":"Teodoro","year":"2019","journal-title":"Environ. Monit. Assess."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.atmosenv.2015.06.056","article-title":"Land use regression models coupled with meteorology to model spatial and temporal variability of NO2 and PM10 in Changsha, China","volume":"116","author":"Liu","year":"2015","journal-title":"Atmos. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.1016\/S1352-2310(97)00447-0","article-title":"Artificial neural networks (the multilayer perceptron)\u2014A review of applications in the atmospheric sciences","volume":"32","author":"Gardner","year":"1998","journal-title":"Atmos. Environ."},{"key":"ref_37","unstructured":"(2018, June 26). Secretaria del Ambiente de Quito Red Metropolitana de Monitoreo Atmosf\u00e9rico de Quito. Available online: http:\/\/www.quitoambiente.gob.ec\/ambiente\/index.php\/generalidades."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Alvarez, C.I., Teodoro, A., and Tierra, A. (2017, January 5). Evaluation of automatic cloud removal method for high elevation areas in Landsat 8 OLI images to improve environmental indexes computation. Proceedings of the SPIE 10428, Earth Resources and Environmental Remote Sensing\/GIS Applications VIII 1042809, Warsaw, Poland.","DOI":"10.1117\/12.2277844"},{"key":"ref_39","first-page":"257","article-title":"Improving NDVI by removing cirrus clouds with optical remote sensing data from Landsat-8\u2014A case study in Quito, Ecuador","volume":"13","author":"Teodoro","year":"2019","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"131","DOI":"10.5539\/mas.v4n11p131","article-title":"Estimating particulate matter concentration over arid region using satellite remote sensing: A case study in Makkah, Saudi Arabia","volume":"4","author":"Othman","year":"2010","journal-title":"Mod. Appl. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Bilguunmaa, M., Batbayar, J., and Tuya, S. (2014). Estimation of PM10 concentration using satellite data in Ulaanbaatar City. SPIE Asia Pac. Remote Sens., 92591O.","DOI":"10.1117\/12.2069149"},{"key":"ref_42","first-page":"105","article-title":"Uso de Modelos Lineales Generalizados (MLG) para la interpolaci\u00f3n espacial de PM10 utilizando im\u00e1genes satelitales Landsat para la ciudad de Bogot\u00e1, Colombia","volume":"22","year":"2017","journal-title":"Perspectiva Geogr\u00e1fica."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Lee, J.H., Ryu, J.E., Chung, H.I., Choi, Y.Y., Jeon, S.W., and Kim, S.H. (2018, January 30). Development of spatial scaling technique of forest health sample point information. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences\u2014ISPRS Archives, Beijing, China.","DOI":"10.5194\/isprs-archives-XLII-3-751-2018"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"563","DOI":"10.3390\/cli3030563","article-title":"Regional Landsat-Based Drought Monitoring from 1982 to 2014","volume":"3","author":"Ghaleb","year":"2015","journal-title":"Climate"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1109\/TGRS.2007.904834","article-title":"Land surface emissivity retrieval from different VNIR and TIR sensors","volume":"46","author":"Sobrino","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"18149","DOI":"10.1109\/ACCESS.2018.2818741","article-title":"Land Surface Temperature Retrieval from Landsat-8 Data with the Generalized Split-Window Algorithm","volume":"6","author":"Li","year":"2018","journal-title":"IEEE Access"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.proeng.2015.07.350","article-title":"Land use Regression as Method to Model Air Pollution. Previous Results for Gothenburg\/Sweden","volume":"115","author":"Habermann","year":"2015","journal-title":"Procedia Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.atmosenv.2015.01.008","article-title":"Development of land-use regression models for metals associated with airborne particulate matter in a North American city","volume":"106","author":"Zhang","year":"2015","journal-title":"Atmos. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1007\/978-1-62703-059-5_23","article-title":"Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression","volume":"Volume 930","author":"Reisfeld","year":"2013","journal-title":"Computational Toxicology: Volume II."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2013\/425740","article-title":"Review on Methods to Fix Number of Hidden Neurons in Neural Networks","volume":"2013","author":"Sheela","year":"2013","journal-title":"Math. Probl. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.atmosenv.2017.02.028","article-title":"Development of land-use regression models for exposure assessment to ultrafine particles in Rome, Italy","volume":"156","author":"Cattani","year":"2017","journal-title":"Atmos. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"5111","DOI":"10.1021\/acs.est.5b06001","article-title":"Combining Land-Use Regression and Chemical Transport Modeling in a Spatiotemporal Geostatistical Model for Ozone and PM 2.5","volume":"50","author":"Wang","year":"2016","journal-title":"Environ. Sci. Technol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1038\/jes.2014.40","article-title":"Consequences of kriging and land use regression for PM2.5 predictions in epidemiologic analyses: Insights into spatial variability using high-resolution satellite data","volume":"25","author":"Alexeeff","year":"2015","journal-title":"J. Expo. Sci. Environ. Epidemiol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.envint.2018.08.041","article-title":"Bayesian geostatistical modelling of PM10 and PM2.5 surface level concentrations in Europe using high-resolution satellite-derived products","volume":"121","author":"Beloconi","year":"2018","journal-title":"Environ. Int."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1829","DOI":"10.5194\/amt-6-1829-2013","article-title":"MODIS 3 km aerosol product: Algorithm and global perspective","volume":"6","author":"Remer","year":"2013","journal-title":"Atmos. Meas. Tech."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Teodoro, A. (2015, January 26\u201331). A study on the Quality of the Vegetation Index obtainded from MODIS Data. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium, Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326540"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Saucy, A., R\u00f6\u00f6sli, M., K\u00fcnzli, N., Tsai, M.Y., Sieber, C., Olaniyan, T., Baatjies, R., Jeebhay, M., Davey, M., and Fl\u00fcckiger, B. (2018). Land use regression modelling of outdoor NO2 and PM2.5 concentrations in three low income areas in the western cape province, South Africa. Int. J. Environ. Res. Public Health, 15.","DOI":"10.3390\/ijerph15071452"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"16092","DOI":"10.1021\/ie3005379","article-title":"Nonlinear PLS Integrated with Error-Based LSSVM and Its Application to NO2 Modeling","volume":"51","author":"Lv","year":"2012","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_59","unstructured":"Secretaria del Ambiente de Quito (2018). IAMQ\/18."},{"key":"ref_60","unstructured":"Romero, D., and El parque automotor aumenta y complica m\u00e1s la movilidad (2019, June 13). El Comer. Available online: https:\/\/www.elcomercio.com\/actualidad\/aumento-parque-automotor-quito-movilidad.html."},{"key":"ref_61","unstructured":"Todoroski Air Sciences (2019). Air Quality Impact Assessment Sandy Point Quarry Epl Variation, Todoroski Air Sciences."}],"container-title":["Environments"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3298\/6\/7\/85\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:08:02Z","timestamp":1760188082000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3298\/6\/7\/85"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,21]]},"references-count":61,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["environments6070085"],"URL":"https:\/\/doi.org\/10.3390\/environments6070085","relation":{},"ISSN":["2076-3298"],"issn-type":[{"value":"2076-3298","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,21]]}}}