A Streamlined Approach by a Combination of Bioindication and Geostatistical Methods for Assessing Air Contaminants and Their Effects on Human Health in Industrialized Areas: A Case Study in Southern Brazil

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Citações na Scopus
8
Tipo de produção
article
Data de publicação
2017
Título da Revista
ISSN da Revista
Título do Volume
Editora
FRONTIERS MEDIA SA
Autores
FERREIRA, Angelica B.
RIBEIRO, Andreza P.
FERREIRA, Mauricio L.
KNIESS, Claudia T.
QUARESMA, Cristiano C.
LAFORTEZZA, Raffaele
SANTOS, Jose O.
SAIKI, Mitiko
Citação
FRONTIERS IN PLANT SCIENCE, v.8, article ID 1575, 15p, 2017
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
Industrialization in developing countries associated with urban growth results in a number of economic benefits, especially in small or medium-sized cities, but leads to a number of environmental and public health consequences. This problem is further aggravated when adequate infrastructure is lacking to monitor the environmental impacts left by industries and refineries. In this study, a new protocol was designed combining biomonitoring and geostatistics to evaluate the possible effects of shale industry emissions on human health and wellbeing. Futhermore, the traditional and expensive air quality method based on PM2 : 5 measuring was also used to validate the low-cost geostatistical approach. Chemical analysis was performed using Energy Dispersive X-ray Fluorescence Spectrometer (EDXRF) to measure inorganic elements in tree bark and shale retorted samples in Sao Mateus do Sul city, Southern Brazil. Fe, S, and Si were considered potential pollutants in the study area. Distribution maps of element concentrations were generated from the dataset and used to estimate the spatial behavior of Fe, S, and Si and the range from their hot spot(s), highlighting the regions sorrounding the shale refinery. This evidence was also demonstrated in the measurements of PM2 : 5 concentrations, which are in agreement with the information obtained from the biomonitoring and geostatistical model. Factor and descriptive analyses performed on the concentrations of tree bark contaminants suggest that Fe, S, and Si might be used as indicators of industrial emissions. The number of cases of respiratory diseases obtained from local basic health unit were used to assess a possible correlation between shale refinery emissions and cases of repiratory disease. These data are public and may be accessed on the website of the the Brazilian Ministry of Health. Significant associations were found between the health data and refinery activities. The combination of the spatial characterization of air pollution and clinical health data revealed that adverse effects were significant for individuals over 38 years of age. These results also suggest that a protocol designed to monitor urban air quality may be an effective and low-cost strategy in environmentally contaminated cities, especially in low-and middle-income countries.
Palavras-chave
air pollution, environmental monitoring, geostatistical approach, industrial pollutants, urban impact
Referências
  1. Assuncao J. V., 2004, CURSO GESTAO AMBIENT, P101
  2. Barbieri R. L., 2012, ARVORES SAO MATEUS S, P476
  3. Bergamini M. F., 2006, Ecletica Quimica, V31, P45
  4. Bohm P, 1998, ENVIRON POLLUT, V102, P243, DOI 10.1016/S0269-7491(98)00082-7
  5. BOUTRON CF, 1994, GEOCHIM COSMOCHIM AC, V58, P3217, DOI 10.1016/0016-7037(94)90049-3
  6. Brasil-Ministerio da Saude, 2007, DAT SIST INF AMB
  7. Brown JS, 2013, PART FIBRE TOXICOL, V10, DOI 10.1186/1743-8977-10-12
  8. Brown R. J. C., 2007, 036 NPL DQLAS
  9. Calado V., 2003, PLANEJAMENTO EXPT US
  10. CHEN JQ, 2016, ECOL PROCESS, V5
  11. Chow JC, 2002, SCI TOTAL ENVIRON, V287, P177, DOI 10.1016/S0048-9697(01)00982-2
  12. CHOW JC, 1994, ATMOS ENVIRON, V28, P2061, DOI 10.1016/1352-2310(94)90474-X
  13. Clark JS, 2001, SCIENCE, V293, P657, DOI 10.1126/science.293.5530.657
  14. Costa-Neto C., 1983, GEOCHEMISTRY BRAZILI
  15. Dominici F, 2006, JAMA-J AM MED ASSOC, V295, P1127, DOI 10.1001/jama.295.10.1127
  16. Fenn ME, 2009, DEV ENVIRONM SCI, V9, P179, DOI 10.1016/S1474-8177(08)00208-8
  17. Ferreira ML, 2012, J ENVIRON MONITOR, V14, P1959, DOI 10.1039/c2em30026e
  18. Fujiwara FG, 2011, ECOL INDIC, V11, P240, DOI 10.1016/j.ecolind.2010.04.007
  19. Galster G, 2001, HOUS POLICY DEBATE, V12, P681
  20. Goudarzi G, 2016, ENVIRON SCI POLLUT R, V23, P22001, DOI 10.1007/s11356-016-7447-x
  21. Guimaraes ET, 2000, ENVIRON EXP BOT, V44, P1, DOI 10.1016/S0098-8472(00)00050-2
  22. Hoek G, 2008, ATMOS ENVIRON, V42, P7561, DOI 10.1016/j.atmosenv.2008.05.057
  23. Hopke PK, 2009, DEV ENVIRONM SCI, V9, P1, DOI 10.1016/S1474-8177(08)00201-5
  24. Hou Q, 2012, SCI TOTAL ENVIRON, V435, P61, DOI 10.1016/j.scitotenv.2012.06.094
  25. IBGE, 2010, CENS DEM 2010
  26. Johnson R., 1992, APPL MULTIVARIATE ST
  27. Jun MJ, 2013, CITIES, V31, P230, DOI 10.1016/j.cities.2012.06.016
  28. Kadiiska MB, 1997, CHEM RES TOXICOL, V10, P1104, DOI 10.1021/tx970049r
  29. Kang B. W., 1997, J KOREAN SOC ATMOS E, V13, P307
  30. Kang CM, 2004, J AIR WASTE MANAGE, V54, P432
  31. Kim KH, 2015, ENVIRON INT, V74, P136, DOI 10.1016/j.envint.2014.10.005
  32. Krewski D, 2009, NEW ENGL J MED, V360, P413, DOI 10.1056/NEJMe0809178
  33. Kuang YW, 2007, ENVIRON SCI POLLUT R, V14, P270, DOI 10.1065/espr2006.09.344
  34. Kundi M, 2006, ENVIRON HEALTH PERSP, V114, P969
  35. Lafortezza R, 2013, IFOREST, V6, P102, DOI 10.3832/ifor0723-006
  36. Londahl J, 2006, J AEROSOL SCI, V37, P1152, DOI 10.1016/j.jaerosci.2005.11.004
  37. Moreira TCL, 2016, ENVIRON INT, V91, P271, DOI 10.1016/j.envint.2016.03.005
  38. Markert B, 1997, ACS SYM SER, V654, P19
  39. Markert BA, 2003, TRACE METALS OTHER, V6, P3
  40. Markert B, 2011, ENVIRON POLLUT, V159, P1991, DOI 10.1016/j.envpol.2011.02.009
  41. Matheron G., 1971, THEORY REGIONALIZED, V5, P211
  42. Mauad T, 2008, AM J RESP CRIT CARE, V178, P721, DOI 10.1164/rccm.200803-436OC
  43. Mulgrew A., 2009, 10 WHO COLL CTR AIR
  44. Nemmar A, 2001, AM J RESP CRIT CARE, V164, P1665
  45. Norouzi S, 2015, ECOL INDIC, V57, P64, DOI 10.1016/j.ecolind.2015.04.011
  46. Nowak DJ, 2000, P INT TOOLS NAT RES, P714
  47. Oliveira M. F., 2002, ECLET QUIM, V27, P153, DOI 10.1590/S0100-46702002000200013
  48. Osman K. T., 2012, SOILS PRINCIPLES PRO, P97
  49. Ots K, 2007, ENVIRON MONIT ASSESS, V130, P465, DOI 10.1007/s10661-006-9436-x
  50. Pimentel P. M., 2006, Cerâmica, V52, P194, DOI 10.1590/S0366-69132006000300013
  51. Poikolainen J., 1997, WATER AIR SOIL POLL, V60, P337
  52. Querol X, 2001, ATMOS ENVIRON, V35, P6407, DOI 10.1016/S1352-2310(01)00361-2
  53. Ravindra K, 2004, SCI TOTAL ENVIRON, V318, P1, DOI 10.1016/S0048-9697(03)00372-3
  54. Ribeiro AP, 2013, MAR POLLUT BULL, V68, P55, DOI 10.1016/j.marpolbul.2012.12.023
  55. Richardson DHS, 1995, SCI TOTAL ENVIRON, V176, P97, DOI 10.1016/0048-9697(95)04835-9
  56. Sanesi G, 2017, LANDSCAPE RES, V42, P164, DOI 10.1080/01426397.2016.1173658
  57. Sawidis T, 2011, ENVIRON POLLUT, V159, P3560, DOI 10.1016/j.envpol.2011.08.008
  58. Schelle E, 2008, ENVIRON POLLUT, V155, P164, DOI 10.1016/j.envpol.2007.10.036
  59. Schelle E, 2002, INT J ENVIRON AN CH, V82, P785, DOI 10.1080/0306731021000102257
  60. Schulz H, 1999, SCI TOTAL ENVIRON, V232, P49, DOI 10.1016/S0048-9697(99)00109-6
  61. Skrbic B, 2012, ECOL INDIC, V13, P168, DOI 10.1016/j.ecolind.2011.05.023
  62. Snyder EG, 2013, ENVIRON SCI TECHNOL, V47, P11369, DOI 10.1021/es4022602
  63. United States Environmental Protection Agency [USEPA], 1987, EPA540P87001, P642
  64. Wang YF, 2003, ATMOS ENVIRON, V37, P4637, DOI 10.1016/j.atmosenv.2003.07.007
  65. Wasserman JC, 2004, QUIM NOVA, V27, P17, DOI 10.1590/S0100-40422004000100004
  66. World Health Organization [WHO], 2012, BIOM BAS IND EXP CHE
  67. Wolterbeek HT, 1995, SCI TOTAL ENVIRON, V176, P33, DOI 10.1016/0048-9697(95)04828-6
  68. Xu YM, 2009, OIL SHALE, V26, P163, DOI 10.3176/oil.2009.2.08
  69. YEOMANS KA, 1982, J ROY STAT SOC D-STA, V31, P221
  70. Yong R. N., 2003, NATURAL ATTENUATION, P336
  71. Zhang YX, 2008, ENVIRON SCI TECHNOL, V42, P7502, DOI 10.1021/es800126y