Assessment of ambient aerosol sources in two important Atlantic Rain Forest hotspots in the surroundings of a megacity
Between 2010 and 2015, an assessment of ambient aerosol sources was carried in two unique fragments of the Atlantic Rain Forest in the surroundings of the Metropolitan Region of Rio de Janeiro (MRRJ). Airborne particulate matter samples were collected at Serra dos Órgãos National Park ( 43 ° 04 ′ 42...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mateus, Vinícius L. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Heterologous expression of codon optimized Trichoderma reesei Cel6A in Pichia pastoris - Sun, Fubao Fuelbiol ELSEVIER, 2016, Jena |
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| Übergeordnetes Werk: |
volume:56 ; year:2020 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.ufug.2020.126858 |
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ELV05231068X |
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| 520 | |a Between 2010 and 2015, an assessment of ambient aerosol sources was carried in two unique fragments of the Atlantic Rain Forest in the surroundings of the Metropolitan Region of Rio de Janeiro (MRRJ). Airborne particulate matter samples were collected at Serra dos Órgãos National Park ( 43 ° 04 ′ 42.1 ″ W and 22 ° 29 ′ 16.9 ″ S) and Mário Xavier National Forest ( 43 ° 42 ′ 21.8 ″ W and 22 ° 43 ′ 21.7 ″ S). At the former site, PM 10 samples were collected, while at the latter TSP samples were collected due to a particular interest on the preservation of an endangered endemic species of tree frog (Physalaemus soaresi). Elemental composition, inorganic and organic water-soluble compounds were analyzed along with local meteorology variables in order to provide the most relevant variables for particulate matter prediction and its potential sources. For TSP, the main predictors were NO 3 − >Mn >Rad (Global radiation) >Ca 2 + >Precipitation >Mg 2 + . For PM 10 , the main predictors were Gust (Gust wind speed) >NO 3 − >Ca 2 + >Zn >Cu >Ti. Furthermore, trends in the particulate matter were analyzed considering the prevailing winds and sources were evaluated whether intermittent or continuous, using the conditional bivariate probability function (CBPF). With the use of CBPF, recent developed machine learning algorithms (Conditional inference trees – CIT, and Random Forests using a conditional inference framework), and other standard data analysis techniques tuned for air quality exercises, we provide an example case for planning and evaluation of environmental risk assessment by stakeholders. | ||
| 520 | |a Between 2010 and 2015, an assessment of ambient aerosol sources was carried in two unique fragments of the Atlantic Rain Forest in the surroundings of the Metropolitan Region of Rio de Janeiro (MRRJ). Airborne particulate matter samples were collected at Serra dos Órgãos National Park ( 43 ° 04 ′ 42.1 ″ W and 22 ° 29 ′ 16.9 ″ S) and Mário Xavier National Forest ( 43 ° 42 ′ 21.8 ″ W and 22 ° 43 ′ 21.7 ″ S). At the former site, PM 10 samples were collected, while at the latter TSP samples were collected due to a particular interest on the preservation of an endangered endemic species of tree frog (Physalaemus soaresi). Elemental composition, inorganic and organic water-soluble compounds were analyzed along with local meteorology variables in order to provide the most relevant variables for particulate matter prediction and its potential sources. For TSP, the main predictors were NO 3 − >Mn >Rad (Global radiation) >Ca 2 + >Precipitation >Mg 2 + . For PM 10 , the main predictors were Gust (Gust wind speed) >NO 3 − >Ca 2 + >Zn >Cu >Ti. Furthermore, trends in the particulate matter were analyzed considering the prevailing winds and sources were evaluated whether intermittent or continuous, using the conditional bivariate probability function (CBPF). With the use of CBPF, recent developed machine learning algorithms (Conditional inference trees – CIT, and Random Forests using a conditional inference framework), and other standard data analysis techniques tuned for air quality exercises, we provide an example case for planning and evaluation of environmental risk assessment by stakeholders. | ||
| 650 | 7 | |a Atmospheric pollution |2 Elsevier | |
| 650 | 7 | |a Conditional inference trees |2 Elsevier | |
| 650 | 7 | |a Atlantic Rain Forest |2 Elsevier | |
| 650 | 7 | |a Conditional bivariate probability function |2 Elsevier | |
| 650 | 7 | |a Source apportionment |2 Elsevier | |
| 650 | 7 | |a Diagnostic ratios |2 Elsevier | |
| 700 | 1 | |a Gioda, Adriana |4 oth | |
| 700 | 1 | |a Marinho, Helga R. |4 oth | |
| 700 | 1 | |a Rocha, Rafael C.C. |4 oth | |
| 700 | 1 | |a Valles, Thiago V. |4 oth | |
| 700 | 1 | |a I. Prohmann, Ana Clara |4 oth | |
| 700 | 1 | |a dos Santos, Larissa C. |4 oth | |
| 700 | 1 | |a Oliveira, Tatiane B. |4 oth | |
| 700 | 1 | |a Melo, Fernanda M. |4 oth | |
| 700 | 1 | |a Saint’Pierre, Tatiana D. |4 oth | |
| 700 | 1 | |a P.G. Maia, Luiz Francisco |4 oth | |
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10.1016/j.ufug.2020.126858 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001224.pica (DE-627)ELV05231068X (ELSEVIER)S1618-8667(20)30675-0 DE-627 ger DE-627 rakwb eng 610 VZ 004 VZ 31.73 bkl 31.76 bkl 44.32 bkl Mateus, Vinícius L. verfasserin aut Assessment of ambient aerosol sources in two important Atlantic Rain Forest hotspots in the surroundings of a megacity 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Between 2010 and 2015, an assessment of ambient aerosol sources was carried in two unique fragments of the Atlantic Rain Forest in the surroundings of the Metropolitan Region of Rio de Janeiro (MRRJ). Airborne particulate matter samples were collected at Serra dos Órgãos National Park ( 43 ° 04 ′ 42.1 ″ W and 22 ° 29 ′ 16.9 ″ S) and Mário Xavier National Forest ( 43 ° 42 ′ 21.8 ″ W and 22 ° 43 ′ 21.7 ″ S). At the former site, PM 10 samples were collected, while at the latter TSP samples were collected due to a particular interest on the preservation of an endangered endemic species of tree frog (Physalaemus soaresi). Elemental composition, inorganic and organic water-soluble compounds were analyzed along with local meteorology variables in order to provide the most relevant variables for particulate matter prediction and its potential sources. For TSP, the main predictors were NO 3 − >Mn >Rad (Global radiation) >Ca 2 + >Precipitation >Mg 2 + . For PM 10 , the main predictors were Gust (Gust wind speed) >NO 3 − >Ca 2 + >Zn >Cu >Ti. Furthermore, trends in the particulate matter were analyzed considering the prevailing winds and sources were evaluated whether intermittent or continuous, using the conditional bivariate probability function (CBPF). With the use of CBPF, recent developed machine learning algorithms (Conditional inference trees – CIT, and Random Forests using a conditional inference framework), and other standard data analysis techniques tuned for air quality exercises, we provide an example case for planning and evaluation of environmental risk assessment by stakeholders. Between 2010 and 2015, an assessment of ambient aerosol sources was carried in two unique fragments of the Atlantic Rain Forest in the surroundings of the Metropolitan Region of Rio de Janeiro (MRRJ). Airborne particulate matter samples were collected at Serra dos Órgãos National Park ( 43 ° 04 ′ 42.1 ″ W and 22 ° 29 ′ 16.9 ″ S) and Mário Xavier National Forest ( 43 ° 42 ′ 21.8 ″ W and 22 ° 43 ′ 21.7 ″ S). At the former site, PM 10 samples were collected, while at the latter TSP samples were collected due to a particular interest on the preservation of an endangered endemic species of tree frog (Physalaemus soaresi). Elemental composition, inorganic and organic water-soluble compounds were analyzed along with local meteorology variables in order to provide the most relevant variables for particulate matter prediction and its potential sources. For TSP, the main predictors were NO 3 − >Mn >Rad (Global radiation) >Ca 2 + >Precipitation >Mg 2 + . For PM 10 , the main predictors were Gust (Gust wind speed) >NO 3 − >Ca 2 + >Zn >Cu >Ti. Furthermore, trends in the particulate matter were analyzed considering the prevailing winds and sources were evaluated whether intermittent or continuous, using the conditional bivariate probability function (CBPF). With the use of CBPF, recent developed machine learning algorithms (Conditional inference trees – CIT, and Random Forests using a conditional inference framework), and other standard data analysis techniques tuned for air quality exercises, we provide an example case for planning and evaluation of environmental risk assessment by stakeholders. Atmospheric pollution Elsevier Conditional inference trees Elsevier Atlantic Rain Forest Elsevier Conditional bivariate probability function Elsevier Source apportionment Elsevier Diagnostic ratios Elsevier Gioda, Adriana oth Marinho, Helga R. oth Rocha, Rafael C.C. oth Valles, Thiago V. oth I. Prohmann, Ana Clara oth dos Santos, Larissa C. oth Oliveira, Tatiane B. oth Melo, Fernanda M. oth Saint’Pierre, Tatiana D. oth P.G. Maia, Luiz Francisco oth Enthalten in Urban & Fischer Sun, Fubao Fuelbiol ELSEVIER Heterologous expression of codon optimized Trichoderma reesei Cel6A in Pichia pastoris 2016 Jena (DE-627)ELV024163988 volume:56 year:2020 pages:0 https://doi.org/10.1016/j.ufug.2020.126858 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_21 GBV_ILN_31 GBV_ILN_40 GBV_ILN_65 GBV_ILN_74 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2018 31.73 Mathematische Statistik VZ 31.76 Numerische Mathematik VZ 44.32 Medizinische Mathematik medizinische Statistik VZ AR 56 2020 0 |
| spelling |
10.1016/j.ufug.2020.126858 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001224.pica (DE-627)ELV05231068X (ELSEVIER)S1618-8667(20)30675-0 DE-627 ger DE-627 rakwb eng 610 VZ 004 VZ 31.73 bkl 31.76 bkl 44.32 bkl Mateus, Vinícius L. verfasserin aut Assessment of ambient aerosol sources in two important Atlantic Rain Forest hotspots in the surroundings of a megacity 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Between 2010 and 2015, an assessment of ambient aerosol sources was carried in two unique fragments of the Atlantic Rain Forest in the surroundings of the Metropolitan Region of Rio de Janeiro (MRRJ). Airborne particulate matter samples were collected at Serra dos Órgãos National Park ( 43 ° 04 ′ 42.1 ″ W and 22 ° 29 ′ 16.9 ″ S) and Mário Xavier National Forest ( 43 ° 42 ′ 21.8 ″ W and 22 ° 43 ′ 21.7 ″ S). At the former site, PM 10 samples were collected, while at the latter TSP samples were collected due to a particular interest on the preservation of an endangered endemic species of tree frog (Physalaemus soaresi). Elemental composition, inorganic and organic water-soluble compounds were analyzed along with local meteorology variables in order to provide the most relevant variables for particulate matter prediction and its potential sources. For TSP, the main predictors were NO 3 − >Mn >Rad (Global radiation) >Ca 2 + >Precipitation >Mg 2 + . For PM 10 , the main predictors were Gust (Gust wind speed) >NO 3 − >Ca 2 + >Zn >Cu >Ti. Furthermore, trends in the particulate matter were analyzed considering the prevailing winds and sources were evaluated whether intermittent or continuous, using the conditional bivariate probability function (CBPF). With the use of CBPF, recent developed machine learning algorithms (Conditional inference trees – CIT, and Random Forests using a conditional inference framework), and other standard data analysis techniques tuned for air quality exercises, we provide an example case for planning and evaluation of environmental risk assessment by stakeholders. Between 2010 and 2015, an assessment of ambient aerosol sources was carried in two unique fragments of the Atlantic Rain Forest in the surroundings of the Metropolitan Region of Rio de Janeiro (MRRJ). Airborne particulate matter samples were collected at Serra dos Órgãos National Park ( 43 ° 04 ′ 42.1 ″ W and 22 ° 29 ′ 16.9 ″ S) and Mário Xavier National Forest ( 43 ° 42 ′ 21.8 ″ W and 22 ° 43 ′ 21.7 ″ S). At the former site, PM 10 samples were collected, while at the latter TSP samples were collected due to a particular interest on the preservation of an endangered endemic species of tree frog (Physalaemus soaresi). Elemental composition, inorganic and organic water-soluble compounds were analyzed along with local meteorology variables in order to provide the most relevant variables for particulate matter prediction and its potential sources. For TSP, the main predictors were NO 3 − >Mn >Rad (Global radiation) >Ca 2 + >Precipitation >Mg 2 + . For PM 10 , the main predictors were Gust (Gust wind speed) >NO 3 − >Ca 2 + >Zn >Cu >Ti. Furthermore, trends in the particulate matter were analyzed considering the prevailing winds and sources were evaluated whether intermittent or continuous, using the conditional bivariate probability function (CBPF). With the use of CBPF, recent developed machine learning algorithms (Conditional inference trees – CIT, and Random Forests using a conditional inference framework), and other standard data analysis techniques tuned for air quality exercises, we provide an example case for planning and evaluation of environmental risk assessment by stakeholders. Atmospheric pollution Elsevier Conditional inference trees Elsevier Atlantic Rain Forest Elsevier Conditional bivariate probability function Elsevier Source apportionment Elsevier Diagnostic ratios Elsevier Gioda, Adriana oth Marinho, Helga R. oth Rocha, Rafael C.C. oth Valles, Thiago V. oth I. Prohmann, Ana Clara oth dos Santos, Larissa C. oth Oliveira, Tatiane B. oth Melo, Fernanda M. oth Saint’Pierre, Tatiana D. oth P.G. Maia, Luiz Francisco oth Enthalten in Urban & Fischer Sun, Fubao Fuelbiol ELSEVIER Heterologous expression of codon optimized Trichoderma reesei Cel6A in Pichia pastoris 2016 Jena (DE-627)ELV024163988 volume:56 year:2020 pages:0 https://doi.org/10.1016/j.ufug.2020.126858 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_21 GBV_ILN_31 GBV_ILN_40 GBV_ILN_65 GBV_ILN_74 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2018 31.73 Mathematische Statistik VZ 31.76 Numerische Mathematik VZ 44.32 Medizinische Mathematik medizinische Statistik VZ AR 56 2020 0 |
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10.1016/j.ufug.2020.126858 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001224.pica (DE-627)ELV05231068X (ELSEVIER)S1618-8667(20)30675-0 DE-627 ger DE-627 rakwb eng 610 VZ 004 VZ 31.73 bkl 31.76 bkl 44.32 bkl Mateus, Vinícius L. verfasserin aut Assessment of ambient aerosol sources in two important Atlantic Rain Forest hotspots in the surroundings of a megacity 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Between 2010 and 2015, an assessment of ambient aerosol sources was carried in two unique fragments of the Atlantic Rain Forest in the surroundings of the Metropolitan Region of Rio de Janeiro (MRRJ). Airborne particulate matter samples were collected at Serra dos Órgãos National Park ( 43 ° 04 ′ 42.1 ″ W and 22 ° 29 ′ 16.9 ″ S) and Mário Xavier National Forest ( 43 ° 42 ′ 21.8 ″ W and 22 ° 43 ′ 21.7 ″ S). At the former site, PM 10 samples were collected, while at the latter TSP samples were collected due to a particular interest on the preservation of an endangered endemic species of tree frog (Physalaemus soaresi). Elemental composition, inorganic and organic water-soluble compounds were analyzed along with local meteorology variables in order to provide the most relevant variables for particulate matter prediction and its potential sources. For TSP, the main predictors were NO 3 − >Mn >Rad (Global radiation) >Ca 2 + >Precipitation >Mg 2 + . For PM 10 , the main predictors were Gust (Gust wind speed) >NO 3 − >Ca 2 + >Zn >Cu >Ti. Furthermore, trends in the particulate matter were analyzed considering the prevailing winds and sources were evaluated whether intermittent or continuous, using the conditional bivariate probability function (CBPF). With the use of CBPF, recent developed machine learning algorithms (Conditional inference trees – CIT, and Random Forests using a conditional inference framework), and other standard data analysis techniques tuned for air quality exercises, we provide an example case for planning and evaluation of environmental risk assessment by stakeholders. Between 2010 and 2015, an assessment of ambient aerosol sources was carried in two unique fragments of the Atlantic Rain Forest in the surroundings of the Metropolitan Region of Rio de Janeiro (MRRJ). Airborne particulate matter samples were collected at Serra dos Órgãos National Park ( 43 ° 04 ′ 42.1 ″ W and 22 ° 29 ′ 16.9 ″ S) and Mário Xavier National Forest ( 43 ° 42 ′ 21.8 ″ W and 22 ° 43 ′ 21.7 ″ S). At the former site, PM 10 samples were collected, while at the latter TSP samples were collected due to a particular interest on the preservation of an endangered endemic species of tree frog (Physalaemus soaresi). Elemental composition, inorganic and organic water-soluble compounds were analyzed along with local meteorology variables in order to provide the most relevant variables for particulate matter prediction and its potential sources. For TSP, the main predictors were NO 3 − >Mn >Rad (Global radiation) >Ca 2 + >Precipitation >Mg 2 + . For PM 10 , the main predictors were Gust (Gust wind speed) >NO 3 − >Ca 2 + >Zn >Cu >Ti. Furthermore, trends in the particulate matter were analyzed considering the prevailing winds and sources were evaluated whether intermittent or continuous, using the conditional bivariate probability function (CBPF). With the use of CBPF, recent developed machine learning algorithms (Conditional inference trees – CIT, and Random Forests using a conditional inference framework), and other standard data analysis techniques tuned for air quality exercises, we provide an example case for planning and evaluation of environmental risk assessment by stakeholders. Atmospheric pollution Elsevier Conditional inference trees Elsevier Atlantic Rain Forest Elsevier Conditional bivariate probability function Elsevier Source apportionment Elsevier Diagnostic ratios Elsevier Gioda, Adriana oth Marinho, Helga R. oth Rocha, Rafael C.C. oth Valles, Thiago V. oth I. Prohmann, Ana Clara oth dos Santos, Larissa C. oth Oliveira, Tatiane B. oth Melo, Fernanda M. oth Saint’Pierre, Tatiana D. oth P.G. Maia, Luiz Francisco oth Enthalten in Urban & Fischer Sun, Fubao Fuelbiol ELSEVIER Heterologous expression of codon optimized Trichoderma reesei Cel6A in Pichia pastoris 2016 Jena (DE-627)ELV024163988 volume:56 year:2020 pages:0 https://doi.org/10.1016/j.ufug.2020.126858 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_21 GBV_ILN_31 GBV_ILN_40 GBV_ILN_65 GBV_ILN_74 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2018 31.73 Mathematische Statistik VZ 31.76 Numerische Mathematik VZ 44.32 Medizinische Mathematik medizinische Statistik VZ AR 56 2020 0 |
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10.1016/j.ufug.2020.126858 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001224.pica (DE-627)ELV05231068X (ELSEVIER)S1618-8667(20)30675-0 DE-627 ger DE-627 rakwb eng 610 VZ 004 VZ 31.73 bkl 31.76 bkl 44.32 bkl Mateus, Vinícius L. verfasserin aut Assessment of ambient aerosol sources in two important Atlantic Rain Forest hotspots in the surroundings of a megacity 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Between 2010 and 2015, an assessment of ambient aerosol sources was carried in two unique fragments of the Atlantic Rain Forest in the surroundings of the Metropolitan Region of Rio de Janeiro (MRRJ). Airborne particulate matter samples were collected at Serra dos Órgãos National Park ( 43 ° 04 ′ 42.1 ″ W and 22 ° 29 ′ 16.9 ″ S) and Mário Xavier National Forest ( 43 ° 42 ′ 21.8 ″ W and 22 ° 43 ′ 21.7 ″ S). At the former site, PM 10 samples were collected, while at the latter TSP samples were collected due to a particular interest on the preservation of an endangered endemic species of tree frog (Physalaemus soaresi). Elemental composition, inorganic and organic water-soluble compounds were analyzed along with local meteorology variables in order to provide the most relevant variables for particulate matter prediction and its potential sources. For TSP, the main predictors were NO 3 − >Mn >Rad (Global radiation) >Ca 2 + >Precipitation >Mg 2 + . For PM 10 , the main predictors were Gust (Gust wind speed) >NO 3 − >Ca 2 + >Zn >Cu >Ti. Furthermore, trends in the particulate matter were analyzed considering the prevailing winds and sources were evaluated whether intermittent or continuous, using the conditional bivariate probability function (CBPF). With the use of CBPF, recent developed machine learning algorithms (Conditional inference trees – CIT, and Random Forests using a conditional inference framework), and other standard data analysis techniques tuned for air quality exercises, we provide an example case for planning and evaluation of environmental risk assessment by stakeholders. Between 2010 and 2015, an assessment of ambient aerosol sources was carried in two unique fragments of the Atlantic Rain Forest in the surroundings of the Metropolitan Region of Rio de Janeiro (MRRJ). Airborne particulate matter samples were collected at Serra dos Órgãos National Park ( 43 ° 04 ′ 42.1 ″ W and 22 ° 29 ′ 16.9 ″ S) and Mário Xavier National Forest ( 43 ° 42 ′ 21.8 ″ W and 22 ° 43 ′ 21.7 ″ S). At the former site, PM 10 samples were collected, while at the latter TSP samples were collected due to a particular interest on the preservation of an endangered endemic species of tree frog (Physalaemus soaresi). Elemental composition, inorganic and organic water-soluble compounds were analyzed along with local meteorology variables in order to provide the most relevant variables for particulate matter prediction and its potential sources. For TSP, the main predictors were NO 3 − >Mn >Rad (Global radiation) >Ca 2 + >Precipitation >Mg 2 + . For PM 10 , the main predictors were Gust (Gust wind speed) >NO 3 − >Ca 2 + >Zn >Cu >Ti. Furthermore, trends in the particulate matter were analyzed considering the prevailing winds and sources were evaluated whether intermittent or continuous, using the conditional bivariate probability function (CBPF). With the use of CBPF, recent developed machine learning algorithms (Conditional inference trees – CIT, and Random Forests using a conditional inference framework), and other standard data analysis techniques tuned for air quality exercises, we provide an example case for planning and evaluation of environmental risk assessment by stakeholders. Atmospheric pollution Elsevier Conditional inference trees Elsevier Atlantic Rain Forest Elsevier Conditional bivariate probability function Elsevier Source apportionment Elsevier Diagnostic ratios Elsevier Gioda, Adriana oth Marinho, Helga R. oth Rocha, Rafael C.C. oth Valles, Thiago V. oth I. Prohmann, Ana Clara oth dos Santos, Larissa C. oth Oliveira, Tatiane B. oth Melo, Fernanda M. oth Saint’Pierre, Tatiana D. oth P.G. Maia, Luiz Francisco oth Enthalten in Urban & Fischer Sun, Fubao Fuelbiol ELSEVIER Heterologous expression of codon optimized Trichoderma reesei Cel6A in Pichia pastoris 2016 Jena (DE-627)ELV024163988 volume:56 year:2020 pages:0 https://doi.org/10.1016/j.ufug.2020.126858 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_21 GBV_ILN_31 GBV_ILN_40 GBV_ILN_65 GBV_ILN_74 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2018 31.73 Mathematische Statistik VZ 31.76 Numerische Mathematik VZ 44.32 Medizinische Mathematik medizinische Statistik VZ AR 56 2020 0 |
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Between 2010 and 2015, an assessment of ambient aerosol sources was carried in two unique fragments of the Atlantic Rain Forest in the surroundings of the Metropolitan Region of Rio de Janeiro (MRRJ). Airborne particulate matter samples were collected at Serra dos Órgãos National Park ( 43 ° 04 ′ 42.1 ″ W and 22 ° 29 ′ 16.9 ″ S) and Mário Xavier National Forest ( 43 ° 42 ′ 21.8 ″ W and 22 ° 43 ′ 21.7 ″ S). At the former site, PM 10 samples were collected, while at the latter TSP samples were collected due to a particular interest on the preservation of an endangered endemic species of tree frog (Physalaemus soaresi). Elemental composition, inorganic and organic water-soluble compounds were analyzed along with local meteorology variables in order to provide the most relevant variables for particulate matter prediction and its potential sources. For TSP, the main predictors were NO 3 − >Mn >Rad (Global radiation) >Ca 2 + >Precipitation >Mg 2 + . For PM 10 , the main predictors were Gust (Gust wind speed) >NO 3 − >Ca 2 + >Zn >Cu >Ti. Furthermore, trends in the particulate matter were analyzed considering the prevailing winds and sources were evaluated whether intermittent or continuous, using the conditional bivariate probability function (CBPF). With the use of CBPF, recent developed machine learning algorithms (Conditional inference trees – CIT, and Random Forests using a conditional inference framework), and other standard data analysis techniques tuned for air quality exercises, we provide an example case for planning and evaluation of environmental risk assessment by stakeholders. |
| abstractGer |
Between 2010 and 2015, an assessment of ambient aerosol sources was carried in two unique fragments of the Atlantic Rain Forest in the surroundings of the Metropolitan Region of Rio de Janeiro (MRRJ). Airborne particulate matter samples were collected at Serra dos Órgãos National Park ( 43 ° 04 ′ 42.1 ″ W and 22 ° 29 ′ 16.9 ″ S) and Mário Xavier National Forest ( 43 ° 42 ′ 21.8 ″ W and 22 ° 43 ′ 21.7 ″ S). At the former site, PM 10 samples were collected, while at the latter TSP samples were collected due to a particular interest on the preservation of an endangered endemic species of tree frog (Physalaemus soaresi). Elemental composition, inorganic and organic water-soluble compounds were analyzed along with local meteorology variables in order to provide the most relevant variables for particulate matter prediction and its potential sources. For TSP, the main predictors were NO 3 − >Mn >Rad (Global radiation) >Ca 2 + >Precipitation >Mg 2 + . For PM 10 , the main predictors were Gust (Gust wind speed) >NO 3 − >Ca 2 + >Zn >Cu >Ti. Furthermore, trends in the particulate matter were analyzed considering the prevailing winds and sources were evaluated whether intermittent or continuous, using the conditional bivariate probability function (CBPF). With the use of CBPF, recent developed machine learning algorithms (Conditional inference trees – CIT, and Random Forests using a conditional inference framework), and other standard data analysis techniques tuned for air quality exercises, we provide an example case for planning and evaluation of environmental risk assessment by stakeholders. |
| abstract_unstemmed |
Between 2010 and 2015, an assessment of ambient aerosol sources was carried in two unique fragments of the Atlantic Rain Forest in the surroundings of the Metropolitan Region of Rio de Janeiro (MRRJ). Airborne particulate matter samples were collected at Serra dos Órgãos National Park ( 43 ° 04 ′ 42.1 ″ W and 22 ° 29 ′ 16.9 ″ S) and Mário Xavier National Forest ( 43 ° 42 ′ 21.8 ″ W and 22 ° 43 ′ 21.7 ″ S). At the former site, PM 10 samples were collected, while at the latter TSP samples were collected due to a particular interest on the preservation of an endangered endemic species of tree frog (Physalaemus soaresi). Elemental composition, inorganic and organic water-soluble compounds were analyzed along with local meteorology variables in order to provide the most relevant variables for particulate matter prediction and its potential sources. For TSP, the main predictors were NO 3 − >Mn >Rad (Global radiation) >Ca 2 + >Precipitation >Mg 2 + . For PM 10 , the main predictors were Gust (Gust wind speed) >NO 3 − >Ca 2 + >Zn >Cu >Ti. Furthermore, trends in the particulate matter were analyzed considering the prevailing winds and sources were evaluated whether intermittent or continuous, using the conditional bivariate probability function (CBPF). With the use of CBPF, recent developed machine learning algorithms (Conditional inference trees – CIT, and Random Forests using a conditional inference framework), and other standard data analysis techniques tuned for air quality exercises, we provide an example case for planning and evaluation of environmental risk assessment by stakeholders. |
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| title_short |
Assessment of ambient aerosol sources in two important Atlantic Rain Forest hotspots in the surroundings of a megacity |
| url |
https://doi.org/10.1016/j.ufug.2020.126858 |
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| author2 |
Gioda, Adriana Marinho, Helga R. Rocha, Rafael C.C. Valles, Thiago V. I. Prohmann, Ana Clara dos Santos, Larissa C. Oliveira, Tatiane B. Melo, Fernanda M. Saint’Pierre, Tatiana D. P.G. Maia, Luiz Francisco |
| author2Str |
Gioda, Adriana Marinho, Helga R. Rocha, Rafael C.C. Valles, Thiago V. I. Prohmann, Ana Clara dos Santos, Larissa C. Oliveira, Tatiane B. Melo, Fernanda M. Saint’Pierre, Tatiana D. P.G. Maia, Luiz Francisco |
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| doi_str |
10.1016/j.ufug.2020.126858 |
| up_date |
2024-07-06T22:40:00.322Z |
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