Use of geospatial tools to predict the risk of contamination by SARS-CoV-2 in urban cemeteries

Urban cemeteries are increasingly surrounded by areas of high residential density as urbanization continues world-wide. With increasing rates of mortality caused by the novel coronavirus, SARS-CoV-2, urban vertical cemeteries are experiencing interments at an unprecedented rate. Corpses interred in...

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Autores:
Carollo Toscan, Paloma
Neckel, Alcindo
Stolfo Maculan, Laércio
Korcelski, Cleiton
Silva Oliveira, Marcos Leandro
BODAH, ELIANE
William Bodah, Brian
Kujawa, Henrique Aniceto
Gonçalves Jr., Affonso Celso
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/9355
Acceso en línea:
https://hdl.handle.net/11323/9355
https://doi.org/10.1016/j.gsf.2021.101310
https://repositorio.cuc.edu.co/
Palabra clave:
Remote sensing
Reflectance temperature
Atmospheric contamination
Urban environment
SARS-CoV-2
Rights
openAccess
License
Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
id RCUC2_3ce549d7ba791b8297a7250076c412d1
oai_identifier_str oai:repositorio.cuc.edu.co:11323/9355
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.eng.fl_str_mv Use of geospatial tools to predict the risk of contamination by SARS-CoV-2 in urban cemeteries
title Use of geospatial tools to predict the risk of contamination by SARS-CoV-2 in urban cemeteries
spellingShingle Use of geospatial tools to predict the risk of contamination by SARS-CoV-2 in urban cemeteries
Remote sensing
Reflectance temperature
Atmospheric contamination
Urban environment
SARS-CoV-2
title_short Use of geospatial tools to predict the risk of contamination by SARS-CoV-2 in urban cemeteries
title_full Use of geospatial tools to predict the risk of contamination by SARS-CoV-2 in urban cemeteries
title_fullStr Use of geospatial tools to predict the risk of contamination by SARS-CoV-2 in urban cemeteries
title_full_unstemmed Use of geospatial tools to predict the risk of contamination by SARS-CoV-2 in urban cemeteries
title_sort Use of geospatial tools to predict the risk of contamination by SARS-CoV-2 in urban cemeteries
dc.creator.fl_str_mv Carollo Toscan, Paloma
Neckel, Alcindo
Stolfo Maculan, Laércio
Korcelski, Cleiton
Silva Oliveira, Marcos Leandro
BODAH, ELIANE
William Bodah, Brian
Kujawa, Henrique Aniceto
Gonçalves Jr., Affonso Celso
dc.contributor.author.spa.fl_str_mv Carollo Toscan, Paloma
Neckel, Alcindo
Stolfo Maculan, Laércio
Korcelski, Cleiton
Silva Oliveira, Marcos Leandro
BODAH, ELIANE
William Bodah, Brian
Kujawa, Henrique Aniceto
Gonçalves Jr., Affonso Celso
dc.subject.proposal.eng.fl_str_mv Remote sensing
Reflectance temperature
Atmospheric contamination
Urban environment
SARS-CoV-2
topic Remote sensing
Reflectance temperature
Atmospheric contamination
Urban environment
SARS-CoV-2
description Urban cemeteries are increasingly surrounded by areas of high residential density as urbanization continues world-wide. With increasing rates of mortality caused by the novel coronavirus, SARS-CoV-2, urban vertical cemeteries are experiencing interments at an unprecedented rate. Corpses interred in the 3rd to 5th layer of vertical urban cemeteries have the potential to contaminate large adjacent regions. The general objective of this manuscript is to analyze the reflectance of altimetry, normalized difference vegetation index (NDVI) and land surface temperature (LST) in the urban cemeteries and neighbouring areas of the City of Passo Fundo, Rio Grande do Sul, Brazil. It is assumed that the population residing in the vicinity of these cemeteries may be exposed to SARS-CoV-2 contamination through the displacement of microparticles carried by the wind as a corpse is placed in the burial niche or during the first several days of subsequent fluid and gas release through the process of decomposition. The reflectance analyses were performed utilizing Landsat 8 satellite images applied to altimetry, NDVI and LST, for hypothetical examination of possible displacement, transport and subsequent deposition of the SARS-CoV-2 virus. The results showed that two cemeteries within the city, cemeteries A and B could potentially transport SARS-CoV-2 of nanometric structure to neighboring residential areas through wind action. These two cemeteries are located at high relative altitudes in more densely populated regions of the city. The NDVI, which has been shown to control the proliferation of contaminants, proved to be insufficient in these areas, contributing to high LST values. Based on the results of this study, the formation and implementation of public policies that monitor urban cemeteries is suggested in areas that utilize vertical urban cemeteries in order to reduce the further spread of the SARS-CoV-2 virus.
publishDate 2021
dc.date.issued.none.fl_str_mv 2021-09-28
dc.date.accessioned.none.fl_str_mv 2022-07-08T13:32:01Z
dc.date.available.none.fl_str_mv 2022-07-08T13:32:01Z
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
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dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.citation.spa.fl_str_mv Paloma Carollo Toscan, Alcindo Neckel, Laércio Stolfo Maculan, Cleiton Korcelski, Marcos L.S. Oliveira, Eliane Thaines Bodah, Brian William Bodah, Henrique Aniceto Kujawa, Affonso Celso Gonçalves, Use of geospatial tools to predict the risk of contamination by SARS-CoV-2 in urban cemeteries, Geoscience Frontiers, 2021, 101310, ISSN 1674-9871, https://doi.org/10.1016/j.gsf.2021.101310.
dc.identifier.issn.spa.fl_str_mv 1674-9871
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/9355
dc.identifier.url.spa.fl_str_mv https://doi.org/10.1016/j.gsf.2021.101310
dc.identifier.doi.spa.fl_str_mv 10.1016/j.gsf.2021.101310
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv Paloma Carollo Toscan, Alcindo Neckel, Laércio Stolfo Maculan, Cleiton Korcelski, Marcos L.S. Oliveira, Eliane Thaines Bodah, Brian William Bodah, Henrique Aniceto Kujawa, Affonso Celso Gonçalves, Use of geospatial tools to predict the risk of contamination by SARS-CoV-2 in urban cemeteries, Geoscience Frontiers, 2021, 101310, ISSN 1674-9871, https://doi.org/10.1016/j.gsf.2021.101310.
1674-9871
10.1016/j.gsf.2021.101310
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/9355
https://doi.org/10.1016/j.gsf.2021.101310
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Geoscience Frontiers
dc.relation.references.spa.fl_str_mv Abia, A.L.K., Alisoltani, A., Ubomba-Jaswa, E., Dippenaar, M.A., 2019. Microbial life beyond the grave: 16s rrna gene-based metagenomic analysis of bacteria diversity and their functional profiles in cemetery environments. Sci. Total Environ. 655, 831–841.
Adulkongkaew, T., Satapanajaru, T., Charoenhirunyingyos, S., Singhirunnusorn, W., 2020. Effect of land cover composition and building configuration on land surface temperature in an urban-sprawl city, case study in Bangkok Metropolitan Area, Thailand. Heliyon 6 (8), 1–13.
Ahmed, T., Singh, D., 2020. Probability density functions based classification of MODIS NDVI time series data and monitoring of vegetation growth cycle. Adv. Space Res. 66 (4), 873–886.
Alexander, C., 2020. Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST). Int. J. Appl. Earth Obs. Geoinf. 86, 102013.
Alexander, C., 2021. Influence of the proportion, height and proximity of vegetation and buildings on urban land surface temperature. Int. J. Appl. Earth Obs. Geoinf. 95, 1–12.
Anand, U., Adelodun, B., Pivato, A., Suresh, S., Indari, O., Jakhmola, S., Jha, H.C., Jha, P. K., Tripathi, V., Maria, F.D., 2021. A review of the presence of SARS-CoV-2 RNA in wastewater and airborne particulates and its use for virus spreading surveillance. Environ. Res. 196, 110929.
Baak, M., Koopman, R., Snoek, H., Klous, S., 2020. A new correlation coefficient between categorical, ordinal and interval variables with Pearson characteristics. Comput. Stat. Data Anal. 152, 107043.
Balta, J.Y., Blom, G., Davidson, A., Perrault, K., Cryan, J.F., O’mahony, S.M., Cassella, J. P., 2020. Developing a quantitative method to assess the decomposition of embalmed human cadavers. Forensic Chem. 18, 100235.
Bakshi, M.S., 2020. Impact of nanomaterials on ecosystems: mechanistic aspects in vivo. Environ. Res. 182, 109099.
Blanar, K., Prada-Tiedemann, P.A., 2020. Characterization of the volatile odor profile from larval masses in a field decomposition setting. Forensic Chem. 21, 100288.
Barcelo, D., 2020. An environmental and health perspective for COVID-19 outbreak: meteorology and air quality influence, sewage epidemiology indicator, hospitals disinfection, drug therapies and recommendations. J. Environ. Chem. Eng. 8, (4) 104006.
Bermudez-Edo, M., Barnaghi, P., Moessner, K., 2018. Analysing real world data streams with spatio-temporal correlations: entropy vs. Pearson correlation. Autom. Constr. 88, 87–100.
Bonatti, M., Lana, M.A., D’agostini, L.R., de Vasconcelos, A.C.F., Sieber, S., Eufemia, L., da Silva-Rosa, T., Schlindwein, S.L., 2019. Social representations of climate change and climate adaptation plans in southern Brazil: challenges of genuine participation. Urban Clim. 29, 100496.
Calmon, M., 2020. Considerations of coronavirus (COVID-19) impact and the management of the dead in Brazil. Forensic Sci. Int., 100110
Cao, Y., Shao, L., Jones, T., Oliveira, M.L.S., Ge, S., Feng, X., Silva, L.F.O., Bérubé, K., 2021. Multiple relationships between aerosol and COVID-19: a framework for global studies. Gondwana Res. 93, 243–251.
Cazenave, A., 2019. Satellite Altimetry. Encyclopedia of Ocean Sciences (Third Edition) 5, 397–401.
Chatterjee, R.S., Singh, N., Thapa, S., Sharma, D., Kumar, D., 2017. Retrieval of land surface temperature (LST) from landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs. Int. J. Appl. Earth Obs. Geoinf. 58, 264–277.
Chemura, A., Mutanga, O., Dube, T., 2017. Integrating age in the detection and mapping of incongruous patches in coffee (Coffea arabica) plantations usingmulti-temporal Landsat 8 NDVI anomalies. Int. J. Appl. Earth Obs. Geoinf. 57, 1–13.
Chi, Y., Sun, J., Sun, Y., Liu, S., Fu, Z., 2020. Multi-temporal characterization of land surface temperature and its relationships with normalized difference vegetation index and soil moisture content in the Yellow River Delta, China. Glob. Ecol. Conserv. 23, 01092.
Cilek, M.U., Cilek, A., 2021. Analyses of land surface temperature (LST) variability among local climate zones (LCZs) comparing Landsat-8 and ENVI-met model data. Sustain. Cities Soc., 102877–102881
Costa, C.A.G., Teixeira, A.dos.S., Andrade, E.M.de., Lucena, A.M.P.de., Castro, M.A.H. de, 2010. Analysis of vegetation influence on altimetry of SRTM data in watersheds in the semiarid region. Rev. Cienc. Agron. 41 (2), 222–230.
Costa, L., Nunes, L., Ampatzidis, Y., 2020. A new visible band index (vNDVI) for estimating NDVI values on RGB images utilizing genetic algorithms. Comput. Electron. Agric. 172, 105334.
Das, M., Das, A., 2020. Assessing the relationship between local climatic zones (LCZs) and land surface temperature (LST) – a case study of SriniketanSantiniketan Planning Area (SSPA), West Bengal. India. Urban Clim. 32, 100591.
Drageset, A., 2019. The Hereid cemetery: relational agency and topography within the iron age mortuary landscape of Hardanger, Western Norway. J. Hist. Geogr. 66, 81–92.
Duarte, A.L., Schneider, I.L., Artaxo, P., Oliveira, M.L.S., 2021. Spatiotemporal assessment of particulate matter (PM10 and PM2.5) and ozone in a Caribbean urban coastal city. Geosci. Front. 101168, 1–9.
Duncan, J.M.A., Boruff, B., Saunders, A., Sun, Q., Hurley, J., Amati, M., 2019. Turning down the heat: an enhanced understanding of the relationship between urban vegetation and surface temperature at the city scale. Sci. Total Environ. 656, 118–128.
Edelmann, D., Móri, T.F., Székely, G.J., 2021. On relationships between the Pearson and the distance correlation coefficients. Stat. Probab. Lett. 169, 108960.
Embrapa (Brazilian Agricultural Research Corporation), 2021. Climate of Passo Fundo in Brazil. Technical Reports. https://www.embrapa.br/busca-depublicacoes/-/publicacao/939638/base-de-dados-climaticos.
Evensen, K.H., Nordh, H., Skaar, M., 2017. Everyday use of urban cemeteries: a Norwegian case study. Landsc. Urban Plan. 159, 76–84.
Gasparin, F., Cravatte, S., Greiner, E., Perruche, C., Hamon, M., Van Gennip, S., Lellouche, J.M., 2021. Excessive productivity and heat content in tropical Pacific analyses: disentangling the effects of in situ and altimetry assimilation. Ocean Model. 160, 101768.
Gavito, M.E., Paz, H., Barragán, F., Siddique, I., Arreola-Villa, F., Pineda-García, F., Balvanera, P., 2021. Indicators of integrative recovery of vegetation, soil and microclimate in successional fields of a tropical dry forest. For. Ecol. Manag. 479, 118526.
Ghassoun, Y., Löwner, M.O., Weber, S., 2019. Wind direction related parameters improve the performance of a land use regression model for ultrafine particles. Atmos. Pollut. Res. 10 (4), 1180–1189.
Gozdowski, D., Ste˛pien´ , M., Panek, E., Varghese, J., Bodecka, E., Rozbicki, J., Samborski, S., 2020. Comparison of winter wheat NDVI data derived from Landsat 8 and active optical sensor at field scale. Remote Sens. Appl. Soc. Environ. 20, 100409.
Guha, S., Govil, H., Gill, N., Dey, A., 2020. A long-term seasonal analysis on the relationship between LST and NDBI using Landsat data. Quat. Int., 1–36
Guyot, M., Araldi, A., Fusco, G., Thomas, I., 2021. The urban form of Brussels from the street perspective: the role of vegetation in the definition of the urban fabric. Landsc. Urban Plan. 205, 1–13.
He, H., He, D., Jin, J., Smits, K.M., Dyck, M., Wu, Q., Si, B., Lv, J., 2020. Room for improvement: a review and evaluation of 24 soil thermal conductivity parameterization schemes commonly used in land-surface, hydrological, and soil-vegetation-atmosphere transfer models. Earth-Sci. Rev. 211, 103419.
Herbst, K.W., Beckers, G.M.A., Harper, L., Bägli, D.J., Nieuwhof-Leppink, A.J., Kaefer, M., Fossum, M., Kalfa, N., 2020. Don’t be mean, be above average: understanding data distribution and descriptive statistics. J. Pediatr. Urol. 16 (5), 712.
Hino, T.M., 2015. The necrochorume and the environmental management of cemeteries. Esp. On-line IPOG 1 (10).
IBGE (Brazilian Institute of Geography and Statistics), 2021. Brazilian Institute of Geography and Statistics. Demographic Data of 2021 – Brazil. https://cidades. ibge.gov.br/.
Jansi, R.S., Khusro, A., Agastian, P., Alfarhan, A., Al-Dhabi, N.A., Arasu, M.V., Rajagopal, R., Barcelo, D., Al-Tamimi, A., 2021. Emerging paradigms of viral diseases and paramount role of natural resources as antiviral agents. Sci. Total Environ. 759, 143539.
King, A.P., Eckersley, R.J., 2019. Descriptive Statistics III: roc analysis. Statist. Biomed. Eng. Sci., 57–69
Lalwani, A., Gautam, S., 2021. Lockdown during COVID-19 pandemic: a case study from Indian cities shows insignificant effects on urban air quality. Geosci. Front., 101284
Leung, W.W.F., Sun, Q.Q., 2020. Electrostatic charged nanofiber filter for filtering airborne novel coronavirus (COVID-19) and nano-aerosols. Sep. Purif. Technol. 250, 17.
Li, H., Zhou, Y., Jia, G., Zhao, K., Dong, J., 2021. Quantifying the response of surface urban heat island to urbanization using the annual temperature cycle model. Geosci. Front., 101141
Maroni, D., Cardoso, G.T., Neckel, A., Maculan, L.S., Oliveira, M.L.S., Bodah, E.T., Bodah, B.W., Santosh, M., 2021. Land surface temperature and vegetation index as a proxy to microclimate. J. Environ. Chem. Eng. 9, (4) 105796.
Mikszewski, A., Stabile, L., Buonanno, G., Morawska, L., 2021. The airborne contagiousness of respiratory viruses: a comparative analysis and implications for mitigation. Geosci. Front., 101285
Moreno, R., Ojeda, N., Azócar, J., Venegas, C., Inostroza, L., 2020. Application of NDVI for identify potentiality of the urban forest for the design of a green corridors system in intermediary cities of Latin America: case study, Temuco, Chile. Urban For. Urban Green. 55, 126821.
Moreno, T., Gibbons, W., 2021. Aerosol transmission of human pathogens: from miasmata to modern viral pandemics and their preservation potential in the anthropocene record. Geosci. Front., 101282
Nascimento, C.M., Mendes, W.de.S., Silvero, N.E.Q., Poppiel, R.R., Sayão, V.M., Dotto, A.C., Santos, N.V.dos., Amorim, M.T.A., Demattê, J.A.M., 2021. Soil degradation index developed by multitemporal remote sensing images, climate variables, terrain and soil atributes. J. Environ. Manage. 277, 111316.
Neckel, A., Costa, C., Mario, D.N., Sabadin, C.E.S., Bodah, E.T., 2017. Environmental damage and public health threat caused by cemeteries: a proposal of ideal cemeteries for the growing urban sprawl. Urbe 9 (2), 216–230.
Neckel, A., Korcelski, C., Kujawa, H.A., Silva, I.S.da., Prezoto, F., Amorin, A.L.W., Maculan, L.S., Gonçalves, A.C., Bodah, E.T., Bodah, B.W., 2021. Hazardous elements in the soil of urban cemeteries; constructive solutions aimed at sustainability. Chemosphere 262, 128248.
Neckel, A., Silva, J.L.da., Saraiva, P.P., Kujawa, H.A., Araldi, J., Paladini, E.P., 2020. Estimation of the economic value of urban parks in Brazil, the case of the City of Passo Fundo. J. Clean. Prod. 264, 121369.
Nega, W., Hailu, B.T., Fetene, A., 2019. An assessment of the vegetation cover change impact on rainfall and land surface temperature using remote sensing in a subtropical climate, Ethiopia. Remote Sens. Appl. Soc. Environ. 16, 100266.
Oliveira, M.L.S., Neckel, A., Pinto, D., Maculan, L.S., Zanchett, M.R.D., Silva, L.F.O., 2021. Air pollutants and their degradation of a historic building in the largest metropolitan area in Latin America. Chemosphere 277, 130286.
PMPF (City Hall of Passo Fundo), 2021. Passo Fundo Against Coronavirus. http://www.pmpf.rs.gov.br/secretaria.php?c=1360.
Prangnell, J., Mcgowan, G., 2009. Soil temperature calculation for burial site analysis. Forensic Sci. Int. 191 (3), 104–109.
Rae, R.A., 2021. Cemeteries as public urban green space: management, funding and form. Urban For. Urban Green. 61, 127078.
Rodriguez-Galiano, V., Pardo-Iguzquiza, E., Sanchez-Castillo, M., Chica-Olmo, M., Chica-Rivas, M., 2012. Downscaling Landsat 7 ETM+ thermal imagery using land surface temperature and NDVI images. Int. J. Appl. Earth Obs. Geoinf. 18, 515– 527.
Romer, I., White, T.A., Baalousha, M., Chipman, K., Viant, M.R., Lead, J.R., 2011. Aggregation and dispersion of silver nanoparticles in exposure media for aquatic toxicity tests. J. Chromatogr. A. 1218, 4226–4233.
Rosmorduc, V., Srinivasan, M., Richardson, A., Cipollini, P., 2020. The first 25 years of altimetry outreach. Adv. Space Res., 1–12
Sayão, V.M., Santos, N.V.dos., Mendes, W.de.S., Marques, K.P.P., Safanelli, J.L., Poppiel, R.R., Demattê, J.A.M., 2020. Land use/land cover changes and bare soil surface temperature monitoring in southeast Brazil. Geoderma Reg. 22, e00313.
Silva, L.F.O., Pinto, D., Neckel, A., Dotto, G.L., Oliveira, M.L.S., 2020. The impact of air pollution on the rate of degradation of the fortress of Florianópolis Island, Brazil. Chemosphere 251, 126838.
Silva, L.F.O., Santosh, M., Schindler, M., Gasparotto, J., Dotto, G., Oliveira, M.L.S., Hochella, M., 2021. Nanoparticles in fossil and mineral fuel sectors and their impact on environment and human health: a review and perspective. Gondwana Res. 92, 184–201.
Shao, L., Ge, S., Jones, T., Santosh, M., Silva, L.F.O., Cao, Y., Oliveira, M.L.S., Zhang, M., Bérubé, K., 2021. The role of airborne particles and environmental considerations in the transmission of SARS-CoV-2. Geosci. Front., 101189
Shi, Y., Liu, S., Yan, W., Zhao, S., Ning, Y., Peng, X., Chen, W., Chen, L., Hu, X., Fu, B., 2021. Influence of landscape features on urban land surface temperature: scale and neighborhood effects. Sci. Total Environ., 145381
Stern, R.A., Al-Hemoud, A., Alahmad, B., Koutrakis, P., 2021. Levels and particle size distribution of airborne SARS-CoV-2 at a healthcare facility in Kuwait. Sci. Total Environ., 146799
Sussman, H.S., Raghavendra, A., Zhou, L., 2019. Impacts of increased urbanization on surface temperature, vegetation, and aerosols over Bengaluru, India. Remote Sens. Appl. Soc. Environ. 16, 100261.
Toscan, P.C., Neckel, A., Korcelski, C., Maculan, L.S., Maroni, D., Fuga, T.M., Cambrussi, L.P., Kujawa, H.A., Bodah, E.T., Bodah, B.W., 2020. Urban Cemeteries in Southern Brazil: an analysis of planimetric variations, vegetation indices and temperature. J. Civ. Eng. Arc. 14 (11), 617–624.
USGS (United States Geological Survey), 2021. Mapping, Remote Sensing, and Geospatial Data. https://www.usgs.gov/science/science-explorer/Mapping%2C+Remote+Sensing%2C+and+Geospatial+Data.
Vanhellemont, Q., 2020. Combined land surface emissivity and temperature estimation from Landsat 8 OLI and TIRS. ISPRS J. Photogramm. Remote Sens. 166, 390–402.
Vanhellemont, Q., 2020. Combined land surface emissivity and temperature estimation from Landsat 8 OLI and TIRS. ISPRS J. Photogramm. Remote Sens. 166, 390–402.
Wanderley, R.L.N., Domingues, L.M., Joly, C.A., Rocha, H.R.da., 2019. Relationship between land surface temperature and fraction of anthropized area in the Atlantic forest region, Brazil. Plos One 14 (12), 1–19.
WHO (World Health Organisation), 2021. WHO Coronavirus Disease (COVID-19) Dashboard. https://covid19.who.int/.
Wu, Z., Yao, L., Zhuang, M., Ren, Y., 2020. Detecting factors controlling spatial patterns in urban land surface temperatures: a case study of Beijing. Sustain. Cities Soc. 63, 102454.
Yang, J., Ren, J., Sun, D., Xiao, X., Xia, J., Jin, C., Li, X., 2021. Understanding land surface temperature impact factors based on local climate zones. Sustain. Cities Soc. 69, 102818.
Zullo, F., Fazio, G., Romano, B., Marucci, A., Fiorini, L., 2019. Effects of urban growth spatial pattern (UGSP) on the land surface temperature (LST): A study in the Po Valley (Italy). Sci. Total Environ. 650, 1740–1751.
Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., 2020. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N. Engl. J. Med. 382 (8), 727–733.
Zorzi, C.G.C., Neckel, A., Maculan, L.S., Cardoso, G.T., Moro, L.D., Savio, A.A.D., Carrasco, L.D.Z., Oliveira, M.L.S., Bodah, E.T., Bodah, B.W., 2021. Geoenvironmental parametric 3D models of SARS-CoV-2 virus circulation in hospital ventilation systems. Geosci. Front., 101279
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spelling Carollo Toscan, Paloma7e21aaf1cd6fba87696683aae7b44c9fNeckel, Alcindo16a6eca62b96e4116a054abf7804f82eStolfo Maculan, Laércio60f535ac65312fae9f28d05928e8c94cKorcelski, Cleiton143411094d89b90640f1435fc4d1149dSilva Oliveira, Marcos Leandro8593f6412a0dba9dc599d10d381fe9b2BODAH, ELIANE65dffd5ddb45ede0b7d02db4068a1ccb600William Bodah, Brian687d5b7065acda74c1ec900ea2ea292aKujawa, Henrique Aniceto3b2255d56dd588cb88adc4b6a824090e600Gonçalves Jr., Affonso Celso53dda736acb0ae16a4eccc041de8ded66002022-07-08T13:32:01Z2022-07-08T13:32:01Z2021-09-28Paloma Carollo Toscan, Alcindo Neckel, Laércio Stolfo Maculan, Cleiton Korcelski, Marcos L.S. Oliveira, Eliane Thaines Bodah, Brian William Bodah, Henrique Aniceto Kujawa, Affonso Celso Gonçalves, Use of geospatial tools to predict the risk of contamination by SARS-CoV-2 in urban cemeteries, Geoscience Frontiers, 2021, 101310, ISSN 1674-9871, https://doi.org/10.1016/j.gsf.2021.101310.1674-9871https://hdl.handle.net/11323/9355https://doi.org/10.1016/j.gsf.2021.10131010.1016/j.gsf.2021.101310Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Urban cemeteries are increasingly surrounded by areas of high residential density as urbanization continues world-wide. With increasing rates of mortality caused by the novel coronavirus, SARS-CoV-2, urban vertical cemeteries are experiencing interments at an unprecedented rate. Corpses interred in the 3rd to 5th layer of vertical urban cemeteries have the potential to contaminate large adjacent regions. The general objective of this manuscript is to analyze the reflectance of altimetry, normalized difference vegetation index (NDVI) and land surface temperature (LST) in the urban cemeteries and neighbouring areas of the City of Passo Fundo, Rio Grande do Sul, Brazil. It is assumed that the population residing in the vicinity of these cemeteries may be exposed to SARS-CoV-2 contamination through the displacement of microparticles carried by the wind as a corpse is placed in the burial niche or during the first several days of subsequent fluid and gas release through the process of decomposition. The reflectance analyses were performed utilizing Landsat 8 satellite images applied to altimetry, NDVI and LST, for hypothetical examination of possible displacement, transport and subsequent deposition of the SARS-CoV-2 virus. The results showed that two cemeteries within the city, cemeteries A and B could potentially transport SARS-CoV-2 of nanometric structure to neighboring residential areas through wind action. These two cemeteries are located at high relative altitudes in more densely populated regions of the city. The NDVI, which has been shown to control the proliferation of contaminants, proved to be insufficient in these areas, contributing to high LST values. Based on the results of this study, the formation and implementation of public policies that monitor urban cemeteries is suggested in areas that utilize vertical urban cemeteries in order to reduce the further spread of the SARS-CoV-2 virus.16 páginasapplication/pdfengChina University of Geosciences (Beijing) and Peking UniversityChinaAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)© 2021 China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Use of geospatial tools to predict the risk of contamination by SARS-CoV-2 in urban cemeteriesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85https://www.sciencedirect.com/science/article/pii/S1674987121001742Geoscience FrontiersAbia, A.L.K., Alisoltani, A., Ubomba-Jaswa, E., Dippenaar, M.A., 2019. Microbial life beyond the grave: 16s rrna gene-based metagenomic analysis of bacteria diversity and their functional profiles in cemetery environments. Sci. Total Environ. 655, 831–841.Adulkongkaew, T., Satapanajaru, T., Charoenhirunyingyos, S., Singhirunnusorn, W., 2020. Effect of land cover composition and building configuration on land surface temperature in an urban-sprawl city, case study in Bangkok Metropolitan Area, Thailand. Heliyon 6 (8), 1–13.Ahmed, T., Singh, D., 2020. Probability density functions based classification of MODIS NDVI time series data and monitoring of vegetation growth cycle. Adv. Space Res. 66 (4), 873–886.Alexander, C., 2020. Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST). Int. J. Appl. Earth Obs. Geoinf. 86, 102013.Alexander, C., 2021. Influence of the proportion, height and proximity of vegetation and buildings on urban land surface temperature. Int. J. Appl. Earth Obs. Geoinf. 95, 1–12.Anand, U., Adelodun, B., Pivato, A., Suresh, S., Indari, O., Jakhmola, S., Jha, H.C., Jha, P. K., Tripathi, V., Maria, F.D., 2021. A review of the presence of SARS-CoV-2 RNA in wastewater and airborne particulates and its use for virus spreading surveillance. Environ. Res. 196, 110929.Baak, M., Koopman, R., Snoek, H., Klous, S., 2020. A new correlation coefficient between categorical, ordinal and interval variables with Pearson characteristics. Comput. Stat. Data Anal. 152, 107043.Balta, J.Y., Blom, G., Davidson, A., Perrault, K., Cryan, J.F., O’mahony, S.M., Cassella, J. P., 2020. Developing a quantitative method to assess the decomposition of embalmed human cadavers. Forensic Chem. 18, 100235.Bakshi, M.S., 2020. Impact of nanomaterials on ecosystems: mechanistic aspects in vivo. Environ. Res. 182, 109099.Blanar, K., Prada-Tiedemann, P.A., 2020. Characterization of the volatile odor profile from larval masses in a field decomposition setting. Forensic Chem. 21, 100288.Barcelo, D., 2020. An environmental and health perspective for COVID-19 outbreak: meteorology and air quality influence, sewage epidemiology indicator, hospitals disinfection, drug therapies and recommendations. J. Environ. Chem. Eng. 8, (4) 104006.Bermudez-Edo, M., Barnaghi, P., Moessner, K., 2018. Analysing real world data streams with spatio-temporal correlations: entropy vs. Pearson correlation. Autom. Constr. 88, 87–100.Bonatti, M., Lana, M.A., D’agostini, L.R., de Vasconcelos, A.C.F., Sieber, S., Eufemia, L., da Silva-Rosa, T., Schlindwein, S.L., 2019. Social representations of climate change and climate adaptation plans in southern Brazil: challenges of genuine participation. Urban Clim. 29, 100496.Calmon, M., 2020. Considerations of coronavirus (COVID-19) impact and the management of the dead in Brazil. Forensic Sci. Int., 100110Cao, Y., Shao, L., Jones, T., Oliveira, M.L.S., Ge, S., Feng, X., Silva, L.F.O., Bérubé, K., 2021. Multiple relationships between aerosol and COVID-19: a framework for global studies. Gondwana Res. 93, 243–251.Cazenave, A., 2019. Satellite Altimetry. Encyclopedia of Ocean Sciences (Third Edition) 5, 397–401.Chatterjee, R.S., Singh, N., Thapa, S., Sharma, D., Kumar, D., 2017. Retrieval of land surface temperature (LST) from landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs. Int. J. Appl. Earth Obs. Geoinf. 58, 264–277.Chemura, A., Mutanga, O., Dube, T., 2017. Integrating age in the detection and mapping of incongruous patches in coffee (Coffea arabica) plantations usingmulti-temporal Landsat 8 NDVI anomalies. Int. J. Appl. Earth Obs. Geoinf. 57, 1–13.Chi, Y., Sun, J., Sun, Y., Liu, S., Fu, Z., 2020. Multi-temporal characterization of land surface temperature and its relationships with normalized difference vegetation index and soil moisture content in the Yellow River Delta, China. Glob. Ecol. Conserv. 23, 01092.Cilek, M.U., Cilek, A., 2021. Analyses of land surface temperature (LST) variability among local climate zones (LCZs) comparing Landsat-8 and ENVI-met model data. Sustain. Cities Soc., 102877–102881Costa, C.A.G., Teixeira, A.dos.S., Andrade, E.M.de., Lucena, A.M.P.de., Castro, M.A.H. de, 2010. Analysis of vegetation influence on altimetry of SRTM data in watersheds in the semiarid region. Rev. Cienc. Agron. 41 (2), 222–230.Costa, L., Nunes, L., Ampatzidis, Y., 2020. A new visible band index (vNDVI) for estimating NDVI values on RGB images utilizing genetic algorithms. Comput. Electron. Agric. 172, 105334.Das, M., Das, A., 2020. Assessing the relationship between local climatic zones (LCZs) and land surface temperature (LST) – a case study of SriniketanSantiniketan Planning Area (SSPA), West Bengal. India. Urban Clim. 32, 100591.Drageset, A., 2019. The Hereid cemetery: relational agency and topography within the iron age mortuary landscape of Hardanger, Western Norway. J. Hist. Geogr. 66, 81–92.Duarte, A.L., Schneider, I.L., Artaxo, P., Oliveira, M.L.S., 2021. Spatiotemporal assessment of particulate matter (PM10 and PM2.5) and ozone in a Caribbean urban coastal city. Geosci. Front. 101168, 1–9.Duncan, J.M.A., Boruff, B., Saunders, A., Sun, Q., Hurley, J., Amati, M., 2019. Turning down the heat: an enhanced understanding of the relationship between urban vegetation and surface temperature at the city scale. Sci. Total Environ. 656, 118–128.Edelmann, D., Móri, T.F., Székely, G.J., 2021. On relationships between the Pearson and the distance correlation coefficients. Stat. Probab. Lett. 169, 108960.Embrapa (Brazilian Agricultural Research Corporation), 2021. Climate of Passo Fundo in Brazil. Technical Reports. https://www.embrapa.br/busca-depublicacoes/-/publicacao/939638/base-de-dados-climaticos.Evensen, K.H., Nordh, H., Skaar, M., 2017. Everyday use of urban cemeteries: a Norwegian case study. Landsc. Urban Plan. 159, 76–84.Gasparin, F., Cravatte, S., Greiner, E., Perruche, C., Hamon, M., Van Gennip, S., Lellouche, J.M., 2021. Excessive productivity and heat content in tropical Pacific analyses: disentangling the effects of in situ and altimetry assimilation. Ocean Model. 160, 101768.Gavito, M.E., Paz, H., Barragán, F., Siddique, I., Arreola-Villa, F., Pineda-García, F., Balvanera, P., 2021. Indicators of integrative recovery of vegetation, soil and microclimate in successional fields of a tropical dry forest. For. Ecol. Manag. 479, 118526.Ghassoun, Y., Löwner, M.O., Weber, S., 2019. Wind direction related parameters improve the performance of a land use regression model for ultrafine particles. Atmos. Pollut. Res. 10 (4), 1180–1189.Gozdowski, D., Ste˛pien´ , M., Panek, E., Varghese, J., Bodecka, E., Rozbicki, J., Samborski, S., 2020. Comparison of winter wheat NDVI data derived from Landsat 8 and active optical sensor at field scale. Remote Sens. Appl. Soc. Environ. 20, 100409.Guha, S., Govil, H., Gill, N., Dey, A., 2020. A long-term seasonal analysis on the relationship between LST and NDBI using Landsat data. Quat. Int., 1–36Guyot, M., Araldi, A., Fusco, G., Thomas, I., 2021. The urban form of Brussels from the street perspective: the role of vegetation in the definition of the urban fabric. Landsc. Urban Plan. 205, 1–13.He, H., He, D., Jin, J., Smits, K.M., Dyck, M., Wu, Q., Si, B., Lv, J., 2020. Room for improvement: a review and evaluation of 24 soil thermal conductivity parameterization schemes commonly used in land-surface, hydrological, and soil-vegetation-atmosphere transfer models. Earth-Sci. Rev. 211, 103419.Herbst, K.W., Beckers, G.M.A., Harper, L., Bägli, D.J., Nieuwhof-Leppink, A.J., Kaefer, M., Fossum, M., Kalfa, N., 2020. Don’t be mean, be above average: understanding data distribution and descriptive statistics. J. Pediatr. Urol. 16 (5), 712.Hino, T.M., 2015. The necrochorume and the environmental management of cemeteries. Esp. On-line IPOG 1 (10).IBGE (Brazilian Institute of Geography and Statistics), 2021. Brazilian Institute of Geography and Statistics. Demographic Data of 2021 – Brazil. https://cidades. ibge.gov.br/.Jansi, R.S., Khusro, A., Agastian, P., Alfarhan, A., Al-Dhabi, N.A., Arasu, M.V., Rajagopal, R., Barcelo, D., Al-Tamimi, A., 2021. Emerging paradigms of viral diseases and paramount role of natural resources as antiviral agents. Sci. Total Environ. 759, 143539.King, A.P., Eckersley, R.J., 2019. Descriptive Statistics III: roc analysis. Statist. Biomed. Eng. Sci., 57–69Lalwani, A., Gautam, S., 2021. Lockdown during COVID-19 pandemic: a case study from Indian cities shows insignificant effects on urban air quality. Geosci. Front., 101284Leung, W.W.F., Sun, Q.Q., 2020. Electrostatic charged nanofiber filter for filtering airborne novel coronavirus (COVID-19) and nano-aerosols. Sep. Purif. Technol. 250, 17.Li, H., Zhou, Y., Jia, G., Zhao, K., Dong, J., 2021. Quantifying the response of surface urban heat island to urbanization using the annual temperature cycle model. Geosci. Front., 101141Maroni, D., Cardoso, G.T., Neckel, A., Maculan, L.S., Oliveira, M.L.S., Bodah, E.T., Bodah, B.W., Santosh, M., 2021. Land surface temperature and vegetation index as a proxy to microclimate. J. Environ. Chem. Eng. 9, (4) 105796.Mikszewski, A., Stabile, L., Buonanno, G., Morawska, L., 2021. The airborne contagiousness of respiratory viruses: a comparative analysis and implications for mitigation. Geosci. Front., 101285Moreno, R., Ojeda, N., Azócar, J., Venegas, C., Inostroza, L., 2020. Application of NDVI for identify potentiality of the urban forest for the design of a green corridors system in intermediary cities of Latin America: case study, Temuco, Chile. Urban For. Urban Green. 55, 126821.Moreno, T., Gibbons, W., 2021. Aerosol transmission of human pathogens: from miasmata to modern viral pandemics and their preservation potential in the anthropocene record. Geosci. Front., 101282Nascimento, C.M., Mendes, W.de.S., Silvero, N.E.Q., Poppiel, R.R., Sayão, V.M., Dotto, A.C., Santos, N.V.dos., Amorim, M.T.A., Demattê, J.A.M., 2021. Soil degradation index developed by multitemporal remote sensing images, climate variables, terrain and soil atributes. J. Environ. Manage. 277, 111316.Neckel, A., Costa, C., Mario, D.N., Sabadin, C.E.S., Bodah, E.T., 2017. Environmental damage and public health threat caused by cemeteries: a proposal of ideal cemeteries for the growing urban sprawl. Urbe 9 (2), 216–230.Neckel, A., Korcelski, C., Kujawa, H.A., Silva, I.S.da., Prezoto, F., Amorin, A.L.W., Maculan, L.S., Gonçalves, A.C., Bodah, E.T., Bodah, B.W., 2021. Hazardous elements in the soil of urban cemeteries; constructive solutions aimed at sustainability. Chemosphere 262, 128248.Neckel, A., Silva, J.L.da., Saraiva, P.P., Kujawa, H.A., Araldi, J., Paladini, E.P., 2020. Estimation of the economic value of urban parks in Brazil, the case of the City of Passo Fundo. J. Clean. Prod. 264, 121369.Nega, W., Hailu, B.T., Fetene, A., 2019. An assessment of the vegetation cover change impact on rainfall and land surface temperature using remote sensing in a subtropical climate, Ethiopia. Remote Sens. Appl. Soc. Environ. 16, 100266.Oliveira, M.L.S., Neckel, A., Pinto, D., Maculan, L.S., Zanchett, M.R.D., Silva, L.F.O., 2021. Air pollutants and their degradation of a historic building in the largest metropolitan area in Latin America. Chemosphere 277, 130286.PMPF (City Hall of Passo Fundo), 2021. Passo Fundo Against Coronavirus. http://www.pmpf.rs.gov.br/secretaria.php?c=1360.Prangnell, J., Mcgowan, G., 2009. Soil temperature calculation for burial site analysis. Forensic Sci. Int. 191 (3), 104–109.Rae, R.A., 2021. Cemeteries as public urban green space: management, funding and form. Urban For. Urban Green. 61, 127078.Rodriguez-Galiano, V., Pardo-Iguzquiza, E., Sanchez-Castillo, M., Chica-Olmo, M., Chica-Rivas, M., 2012. Downscaling Landsat 7 ETM+ thermal imagery using land surface temperature and NDVI images. Int. J. Appl. Earth Obs. Geoinf. 18, 515– 527.Romer, I., White, T.A., Baalousha, M., Chipman, K., Viant, M.R., Lead, J.R., 2011. Aggregation and dispersion of silver nanoparticles in exposure media for aquatic toxicity tests. J. Chromatogr. A. 1218, 4226–4233.Rosmorduc, V., Srinivasan, M., Richardson, A., Cipollini, P., 2020. The first 25 years of altimetry outreach. Adv. Space Res., 1–12Sayão, V.M., Santos, N.V.dos., Mendes, W.de.S., Marques, K.P.P., Safanelli, J.L., Poppiel, R.R., Demattê, J.A.M., 2020. Land use/land cover changes and bare soil surface temperature monitoring in southeast Brazil. Geoderma Reg. 22, e00313.Silva, L.F.O., Pinto, D., Neckel, A., Dotto, G.L., Oliveira, M.L.S., 2020. The impact of air pollution on the rate of degradation of the fortress of Florianópolis Island, Brazil. Chemosphere 251, 126838.Silva, L.F.O., Santosh, M., Schindler, M., Gasparotto, J., Dotto, G., Oliveira, M.L.S., Hochella, M., 2021. Nanoparticles in fossil and mineral fuel sectors and their impact on environment and human health: a review and perspective. Gondwana Res. 92, 184–201.Shao, L., Ge, S., Jones, T., Santosh, M., Silva, L.F.O., Cao, Y., Oliveira, M.L.S., Zhang, M., Bérubé, K., 2021. The role of airborne particles and environmental considerations in the transmission of SARS-CoV-2. Geosci. Front., 101189Shi, Y., Liu, S., Yan, W., Zhao, S., Ning, Y., Peng, X., Chen, W., Chen, L., Hu, X., Fu, B., 2021. Influence of landscape features on urban land surface temperature: scale and neighborhood effects. Sci. Total Environ., 145381Stern, R.A., Al-Hemoud, A., Alahmad, B., Koutrakis, P., 2021. Levels and particle size distribution of airborne SARS-CoV-2 at a healthcare facility in Kuwait. Sci. Total Environ., 146799Sussman, H.S., Raghavendra, A., Zhou, L., 2019. Impacts of increased urbanization on surface temperature, vegetation, and aerosols over Bengaluru, India. Remote Sens. Appl. Soc. Environ. 16, 100261.Toscan, P.C., Neckel, A., Korcelski, C., Maculan, L.S., Maroni, D., Fuga, T.M., Cambrussi, L.P., Kujawa, H.A., Bodah, E.T., Bodah, B.W., 2020. Urban Cemeteries in Southern Brazil: an analysis of planimetric variations, vegetation indices and temperature. J. Civ. Eng. Arc. 14 (11), 617–624.USGS (United States Geological Survey), 2021. Mapping, Remote Sensing, and Geospatial Data. https://www.usgs.gov/science/science-explorer/Mapping%2C+Remote+Sensing%2C+and+Geospatial+Data.Vanhellemont, Q., 2020. Combined land surface emissivity and temperature estimation from Landsat 8 OLI and TIRS. ISPRS J. Photogramm. Remote Sens. 166, 390–402.Vanhellemont, Q., 2020. Combined land surface emissivity and temperature estimation from Landsat 8 OLI and TIRS. ISPRS J. Photogramm. Remote Sens. 166, 390–402.Wanderley, R.L.N., Domingues, L.M., Joly, C.A., Rocha, H.R.da., 2019. Relationship between land surface temperature and fraction of anthropized area in the Atlantic forest region, Brazil. Plos One 14 (12), 1–19.WHO (World Health Organisation), 2021. WHO Coronavirus Disease (COVID-19) Dashboard. https://covid19.who.int/.Wu, Z., Yao, L., Zhuang, M., Ren, Y., 2020. Detecting factors controlling spatial patterns in urban land surface temperatures: a case study of Beijing. Sustain. Cities Soc. 63, 102454.Yang, J., Ren, J., Sun, D., Xiao, X., Xia, J., Jin, C., Li, X., 2021. Understanding land surface temperature impact factors based on local climate zones. Sustain. Cities Soc. 69, 102818.Zullo, F., Fazio, G., Romano, B., Marucci, A., Fiorini, L., 2019. Effects of urban growth spatial pattern (UGSP) on the land surface temperature (LST): A study in the Po Valley (Italy). Sci. Total Environ. 650, 1740–1751.Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., 2020. A Novel Coronavirus from Patients with Pneumonia in China, 2019. N. Engl. J. Med. 382 (8), 727–733.Zorzi, C.G.C., Neckel, A., Maculan, L.S., Cardoso, G.T., Moro, L.D., Savio, A.A.D., Carrasco, L.D.Z., Oliveira, M.L.S., Bodah, E.T., Bodah, B.W., 2021. Geoenvironmental parametric 3D models of SARS-CoV-2 virus circulation in hospital ventilation systems. Geosci. 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