Characterising personal, household, and community PM 2.5 exposure in one urban and two rural communities in China
Background: Cooking and heating in households contribute importantly to air pollution exposure worldwide. However, there is insufficient investigation of measured fine particulate matter (PM2.5) exposure levels, variability, seasonality, and inter-spatial dynamics associated with these behaviours.Me...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chan, Ka Hung [verfasserIn] Xia, Xi [verfasserIn] Liu, Cong [verfasserIn] Kan, Haidong [verfasserIn] Doherty, Aiden [verfasserIn] Yim, Steve Hung Lam [verfasserIn] Wright, Neil [verfasserIn] Kartsonaki, Christiana [verfasserIn] Yang, Xiaoming [verfasserIn] Stevens, Rebecca [verfasserIn] Chang, Xiaoyu [verfasserIn] Sun, Dianjianyi [verfasserIn] Yu, Canqing [verfasserIn] Lv, Jun [verfasserIn] Li, Liming [verfasserIn] Ho, Kin-Fai [verfasserIn] Lam, Kin Bong Hubert [verfasserIn] Chen, Zhengming [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: The science of the total environment - Amsterdam [u.a.] : Elsevier Science, 1972, 904 |
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| Übergeordnetes Werk: |
volume:904 |
| DOI / URN: |
10.1016/j.scitotenv.2023.166647 |
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ELV065351630 |
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| 100 | 1 | |a Chan, Ka Hung |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Characterising personal, household, and community PM 2.5 exposure in one urban and two rural communities in China |
| 264 | 1 | |c 2023 | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
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| 520 | |a Background: Cooking and heating in households contribute importantly to air pollution exposure worldwide. However, there is insufficient investigation of measured fine particulate matter (PM2.5) exposure levels, variability, seasonality, and inter-spatial dynamics associated with these behaviours.Methods: We undertook parallel measurements of personal, household (kitchen and living room), and community PM2.5 in summer (May–September 2017) and winter (November 2017-Janauary 2018) in 477 participants from one urban and two rural communities in China. After stringent data cleaning, there were 67,326–80,980 person-hours (ntotal = 441; nsummer = 384; nwinter = 364; 307 had repeated PM2.5 data in both seasons) of processed data per microenvironment. Age- and sex-adjusted geometric means of PM2.5 were calculated by key participant characteristics, overall and by season. Spearman correlation coefficients between PM2.5 levels across different microenvironments were computed.Findings: Overall, 26.4 % reported use of solid fuel for both cooking and heating. Solid fuel users had 92 % higher personal and kitchen 24-h average PM2.5 exposure than clean fuel users. Similarly, they also had a greater increase (83 % vs 26 %) in personal and household PM2.5 from summer to winter, whereas community levels of PM2.5 were 2–4 times higher in winter across different fuel categories. Compared with clean fuel users, solid fuel users had markedly higher weighted annual average PM2.5 exposure at personal (78.2 [95 % CI 71.6–85.3] μg/m3 vs 41.6 [37.3–46.5] μg/m3), kitchen (102.4 [90.4–116.0] μg/m3 vs 52.3 [44.8–61.2] μg/m3) and living room (62.1 [57.3–67.3] μg/m3 vs 41.0 [37.1–45.3] μg/m3) microenvironments. There was a remarkable diurnal variability in PM2.5 exposure among the participants, with 5-min moving average from 10 μg/m3 to 700–1200 μg/m3 across different microenvironments. Personal PM2.5 was moderately correlated with living room (Spearman r: 0.64–0.66) and kitchen (0.52–0.59) levels, but only weakly correlated with community levels, especially in summer (0.15–0.34) and among solid fuel users (0.11–0.31).Conclusion: Solid fuel use for cooking and heating was associated with substantially higher personal and household PM2.5 exposure than clean fuel users. Household PM2.5 appeared a better proxy of personal exposure than community PM2.5. | ||
| 650 | 4 | |a Fine particulate matter | |
| 650 | 4 | |a Exposure assessment | |
| 650 | 4 | |a Wearable sensor | |
| 650 | 4 | |a Solid fuels | |
| 650 | 4 | |a Cooking | |
| 650 | 4 | |a Heating | |
| 700 | 1 | |a Xia, Xi |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Cong |e verfasserin |4 aut | |
| 700 | 1 | |a Kan, Haidong |e verfasserin |4 aut | |
| 700 | 1 | |a Doherty, Aiden |e verfasserin |4 aut | |
| 700 | 1 | |a Yim, Steve Hung Lam |e verfasserin |4 aut | |
| 700 | 1 | |a Wright, Neil |e verfasserin |4 aut | |
| 700 | 1 | |a Kartsonaki, Christiana |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Xiaoming |e verfasserin |4 aut | |
| 700 | 1 | |a Stevens, Rebecca |e verfasserin |4 aut | |
| 700 | 1 | |a Chang, Xiaoyu |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Dianjianyi |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Canqing |e verfasserin |4 aut | |
| 700 | 1 | |a Lv, Jun |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Liming |e verfasserin |4 aut | |
| 700 | 1 | |a Ho, Kin-Fai |e verfasserin |4 aut | |
| 700 | 1 | |a Lam, Kin Bong Hubert |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Zhengming |e verfasserin |4 aut | |
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10.1016/j.scitotenv.2023.166647 doi (DE-627)ELV065351630 (ELSEVIER)S0048-9697(23)05272-5 DE-627 ger DE-627 rda eng 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Chan, Ka Hung verfasserin aut Characterising personal, household, and community PM 2.5 exposure in one urban and two rural communities in China 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Cooking and heating in households contribute importantly to air pollution exposure worldwide. However, there is insufficient investigation of measured fine particulate matter (PM2.5) exposure levels, variability, seasonality, and inter-spatial dynamics associated with these behaviours.Methods: We undertook parallel measurements of personal, household (kitchen and living room), and community PM2.5 in summer (May–September 2017) and winter (November 2017-Janauary 2018) in 477 participants from one urban and two rural communities in China. After stringent data cleaning, there were 67,326–80,980 person-hours (ntotal = 441; nsummer = 384; nwinter = 364; 307 had repeated PM2.5 data in both seasons) of processed data per microenvironment. Age- and sex-adjusted geometric means of PM2.5 were calculated by key participant characteristics, overall and by season. Spearman correlation coefficients between PM2.5 levels across different microenvironments were computed.Findings: Overall, 26.4 % reported use of solid fuel for both cooking and heating. Solid fuel users had 92 % higher personal and kitchen 24-h average PM2.5 exposure than clean fuel users. Similarly, they also had a greater increase (83 % vs 26 %) in personal and household PM2.5 from summer to winter, whereas community levels of PM2.5 were 2–4 times higher in winter across different fuel categories. Compared with clean fuel users, solid fuel users had markedly higher weighted annual average PM2.5 exposure at personal (78.2 [95 % CI 71.6–85.3] μg/m3 vs 41.6 [37.3–46.5] μg/m3), kitchen (102.4 [90.4–116.0] μg/m3 vs 52.3 [44.8–61.2] μg/m3) and living room (62.1 [57.3–67.3] μg/m3 vs 41.0 [37.1–45.3] μg/m3) microenvironments. There was a remarkable diurnal variability in PM2.5 exposure among the participants, with 5-min moving average from 10 μg/m3 to 700–1200 μg/m3 across different microenvironments. Personal PM2.5 was moderately correlated with living room (Spearman r: 0.64–0.66) and kitchen (0.52–0.59) levels, but only weakly correlated with community levels, especially in summer (0.15–0.34) and among solid fuel users (0.11–0.31).Conclusion: Solid fuel use for cooking and heating was associated with substantially higher personal and household PM2.5 exposure than clean fuel users. Household PM2.5 appeared a better proxy of personal exposure than community PM2.5. Fine particulate matter Exposure assessment Wearable sensor Solid fuels Cooking Heating Xia, Xi verfasserin aut Liu, Cong verfasserin aut Kan, Haidong verfasserin aut Doherty, Aiden verfasserin aut Yim, Steve Hung Lam verfasserin aut Wright, Neil verfasserin aut Kartsonaki, Christiana verfasserin aut Yang, Xiaoming verfasserin aut Stevens, Rebecca verfasserin aut Chang, Xiaoyu verfasserin aut Sun, Dianjianyi verfasserin aut Yu, Canqing verfasserin aut Lv, Jun verfasserin aut Li, Liming verfasserin aut Ho, Kin-Fai verfasserin aut Lam, Kin Bong Hubert verfasserin aut Chen, Zhengming verfasserin aut Enthalten in The science of the total environment Amsterdam [u.a.] : Elsevier Science, 1972 904 Online-Ressource (DE-627)306591456 (DE-600)1498726-0 (DE-576)081953178 1879-1026 nnns volume:904 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 904 |
| spelling |
10.1016/j.scitotenv.2023.166647 doi (DE-627)ELV065351630 (ELSEVIER)S0048-9697(23)05272-5 DE-627 ger DE-627 rda eng 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Chan, Ka Hung verfasserin aut Characterising personal, household, and community PM 2.5 exposure in one urban and two rural communities in China 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Cooking and heating in households contribute importantly to air pollution exposure worldwide. However, there is insufficient investigation of measured fine particulate matter (PM2.5) exposure levels, variability, seasonality, and inter-spatial dynamics associated with these behaviours.Methods: We undertook parallel measurements of personal, household (kitchen and living room), and community PM2.5 in summer (May–September 2017) and winter (November 2017-Janauary 2018) in 477 participants from one urban and two rural communities in China. After stringent data cleaning, there were 67,326–80,980 person-hours (ntotal = 441; nsummer = 384; nwinter = 364; 307 had repeated PM2.5 data in both seasons) of processed data per microenvironment. Age- and sex-adjusted geometric means of PM2.5 were calculated by key participant characteristics, overall and by season. Spearman correlation coefficients between PM2.5 levels across different microenvironments were computed.Findings: Overall, 26.4 % reported use of solid fuel for both cooking and heating. Solid fuel users had 92 % higher personal and kitchen 24-h average PM2.5 exposure than clean fuel users. Similarly, they also had a greater increase (83 % vs 26 %) in personal and household PM2.5 from summer to winter, whereas community levels of PM2.5 were 2–4 times higher in winter across different fuel categories. Compared with clean fuel users, solid fuel users had markedly higher weighted annual average PM2.5 exposure at personal (78.2 [95 % CI 71.6–85.3] μg/m3 vs 41.6 [37.3–46.5] μg/m3), kitchen (102.4 [90.4–116.0] μg/m3 vs 52.3 [44.8–61.2] μg/m3) and living room (62.1 [57.3–67.3] μg/m3 vs 41.0 [37.1–45.3] μg/m3) microenvironments. There was a remarkable diurnal variability in PM2.5 exposure among the participants, with 5-min moving average from 10 μg/m3 to 700–1200 μg/m3 across different microenvironments. Personal PM2.5 was moderately correlated with living room (Spearman r: 0.64–0.66) and kitchen (0.52–0.59) levels, but only weakly correlated with community levels, especially in summer (0.15–0.34) and among solid fuel users (0.11–0.31).Conclusion: Solid fuel use for cooking and heating was associated with substantially higher personal and household PM2.5 exposure than clean fuel users. Household PM2.5 appeared a better proxy of personal exposure than community PM2.5. Fine particulate matter Exposure assessment Wearable sensor Solid fuels Cooking Heating Xia, Xi verfasserin aut Liu, Cong verfasserin aut Kan, Haidong verfasserin aut Doherty, Aiden verfasserin aut Yim, Steve Hung Lam verfasserin aut Wright, Neil verfasserin aut Kartsonaki, Christiana verfasserin aut Yang, Xiaoming verfasserin aut Stevens, Rebecca verfasserin aut Chang, Xiaoyu verfasserin aut Sun, Dianjianyi verfasserin aut Yu, Canqing verfasserin aut Lv, Jun verfasserin aut Li, Liming verfasserin aut Ho, Kin-Fai verfasserin aut Lam, Kin Bong Hubert verfasserin aut Chen, Zhengming verfasserin aut Enthalten in The science of the total environment Amsterdam [u.a.] : Elsevier Science, 1972 904 Online-Ressource (DE-627)306591456 (DE-600)1498726-0 (DE-576)081953178 1879-1026 nnns volume:904 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 904 |
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10.1016/j.scitotenv.2023.166647 doi (DE-627)ELV065351630 (ELSEVIER)S0048-9697(23)05272-5 DE-627 ger DE-627 rda eng 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Chan, Ka Hung verfasserin aut Characterising personal, household, and community PM 2.5 exposure in one urban and two rural communities in China 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Cooking and heating in households contribute importantly to air pollution exposure worldwide. However, there is insufficient investigation of measured fine particulate matter (PM2.5) exposure levels, variability, seasonality, and inter-spatial dynamics associated with these behaviours.Methods: We undertook parallel measurements of personal, household (kitchen and living room), and community PM2.5 in summer (May–September 2017) and winter (November 2017-Janauary 2018) in 477 participants from one urban and two rural communities in China. After stringent data cleaning, there were 67,326–80,980 person-hours (ntotal = 441; nsummer = 384; nwinter = 364; 307 had repeated PM2.5 data in both seasons) of processed data per microenvironment. Age- and sex-adjusted geometric means of PM2.5 were calculated by key participant characteristics, overall and by season. Spearman correlation coefficients between PM2.5 levels across different microenvironments were computed.Findings: Overall, 26.4 % reported use of solid fuel for both cooking and heating. Solid fuel users had 92 % higher personal and kitchen 24-h average PM2.5 exposure than clean fuel users. Similarly, they also had a greater increase (83 % vs 26 %) in personal and household PM2.5 from summer to winter, whereas community levels of PM2.5 were 2–4 times higher in winter across different fuel categories. Compared with clean fuel users, solid fuel users had markedly higher weighted annual average PM2.5 exposure at personal (78.2 [95 % CI 71.6–85.3] μg/m3 vs 41.6 [37.3–46.5] μg/m3), kitchen (102.4 [90.4–116.0] μg/m3 vs 52.3 [44.8–61.2] μg/m3) and living room (62.1 [57.3–67.3] μg/m3 vs 41.0 [37.1–45.3] μg/m3) microenvironments. There was a remarkable diurnal variability in PM2.5 exposure among the participants, with 5-min moving average from 10 μg/m3 to 700–1200 μg/m3 across different microenvironments. Personal PM2.5 was moderately correlated with living room (Spearman r: 0.64–0.66) and kitchen (0.52–0.59) levels, but only weakly correlated with community levels, especially in summer (0.15–0.34) and among solid fuel users (0.11–0.31).Conclusion: Solid fuel use for cooking and heating was associated with substantially higher personal and household PM2.5 exposure than clean fuel users. Household PM2.5 appeared a better proxy of personal exposure than community PM2.5. Fine particulate matter Exposure assessment Wearable sensor Solid fuels Cooking Heating Xia, Xi verfasserin aut Liu, Cong verfasserin aut Kan, Haidong verfasserin aut Doherty, Aiden verfasserin aut Yim, Steve Hung Lam verfasserin aut Wright, Neil verfasserin aut Kartsonaki, Christiana verfasserin aut Yang, Xiaoming verfasserin aut Stevens, Rebecca verfasserin aut Chang, Xiaoyu verfasserin aut Sun, Dianjianyi verfasserin aut Yu, Canqing verfasserin aut Lv, Jun verfasserin aut Li, Liming verfasserin aut Ho, Kin-Fai verfasserin aut Lam, Kin Bong Hubert verfasserin aut Chen, Zhengming verfasserin aut Enthalten in The science of the total environment Amsterdam [u.a.] : Elsevier Science, 1972 904 Online-Ressource (DE-627)306591456 (DE-600)1498726-0 (DE-576)081953178 1879-1026 nnns volume:904 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 904 |
| allfieldsGer |
10.1016/j.scitotenv.2023.166647 doi (DE-627)ELV065351630 (ELSEVIER)S0048-9697(23)05272-5 DE-627 ger DE-627 rda eng 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Chan, Ka Hung verfasserin aut Characterising personal, household, and community PM 2.5 exposure in one urban and two rural communities in China 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Cooking and heating in households contribute importantly to air pollution exposure worldwide. However, there is insufficient investigation of measured fine particulate matter (PM2.5) exposure levels, variability, seasonality, and inter-spatial dynamics associated with these behaviours.Methods: We undertook parallel measurements of personal, household (kitchen and living room), and community PM2.5 in summer (May–September 2017) and winter (November 2017-Janauary 2018) in 477 participants from one urban and two rural communities in China. After stringent data cleaning, there were 67,326–80,980 person-hours (ntotal = 441; nsummer = 384; nwinter = 364; 307 had repeated PM2.5 data in both seasons) of processed data per microenvironment. Age- and sex-adjusted geometric means of PM2.5 were calculated by key participant characteristics, overall and by season. Spearman correlation coefficients between PM2.5 levels across different microenvironments were computed.Findings: Overall, 26.4 % reported use of solid fuel for both cooking and heating. Solid fuel users had 92 % higher personal and kitchen 24-h average PM2.5 exposure than clean fuel users. Similarly, they also had a greater increase (83 % vs 26 %) in personal and household PM2.5 from summer to winter, whereas community levels of PM2.5 were 2–4 times higher in winter across different fuel categories. Compared with clean fuel users, solid fuel users had markedly higher weighted annual average PM2.5 exposure at personal (78.2 [95 % CI 71.6–85.3] μg/m3 vs 41.6 [37.3–46.5] μg/m3), kitchen (102.4 [90.4–116.0] μg/m3 vs 52.3 [44.8–61.2] μg/m3) and living room (62.1 [57.3–67.3] μg/m3 vs 41.0 [37.1–45.3] μg/m3) microenvironments. There was a remarkable diurnal variability in PM2.5 exposure among the participants, with 5-min moving average from 10 μg/m3 to 700–1200 μg/m3 across different microenvironments. Personal PM2.5 was moderately correlated with living room (Spearman r: 0.64–0.66) and kitchen (0.52–0.59) levels, but only weakly correlated with community levels, especially in summer (0.15–0.34) and among solid fuel users (0.11–0.31).Conclusion: Solid fuel use for cooking and heating was associated with substantially higher personal and household PM2.5 exposure than clean fuel users. Household PM2.5 appeared a better proxy of personal exposure than community PM2.5. Fine particulate matter Exposure assessment Wearable sensor Solid fuels Cooking Heating Xia, Xi verfasserin aut Liu, Cong verfasserin aut Kan, Haidong verfasserin aut Doherty, Aiden verfasserin aut Yim, Steve Hung Lam verfasserin aut Wright, Neil verfasserin aut Kartsonaki, Christiana verfasserin aut Yang, Xiaoming verfasserin aut Stevens, Rebecca verfasserin aut Chang, Xiaoyu verfasserin aut Sun, Dianjianyi verfasserin aut Yu, Canqing verfasserin aut Lv, Jun verfasserin aut Li, Liming verfasserin aut Ho, Kin-Fai verfasserin aut Lam, Kin Bong Hubert verfasserin aut Chen, Zhengming verfasserin aut Enthalten in The science of the total environment Amsterdam [u.a.] : Elsevier Science, 1972 904 Online-Ressource (DE-627)306591456 (DE-600)1498726-0 (DE-576)081953178 1879-1026 nnns volume:904 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 904 |
| allfieldsSound |
10.1016/j.scitotenv.2023.166647 doi (DE-627)ELV065351630 (ELSEVIER)S0048-9697(23)05272-5 DE-627 ger DE-627 rda eng 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Chan, Ka Hung verfasserin aut Characterising personal, household, and community PM 2.5 exposure in one urban and two rural communities in China 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Cooking and heating in households contribute importantly to air pollution exposure worldwide. However, there is insufficient investigation of measured fine particulate matter (PM2.5) exposure levels, variability, seasonality, and inter-spatial dynamics associated with these behaviours.Methods: We undertook parallel measurements of personal, household (kitchen and living room), and community PM2.5 in summer (May–September 2017) and winter (November 2017-Janauary 2018) in 477 participants from one urban and two rural communities in China. After stringent data cleaning, there were 67,326–80,980 person-hours (ntotal = 441; nsummer = 384; nwinter = 364; 307 had repeated PM2.5 data in both seasons) of processed data per microenvironment. Age- and sex-adjusted geometric means of PM2.5 were calculated by key participant characteristics, overall and by season. Spearman correlation coefficients between PM2.5 levels across different microenvironments were computed.Findings: Overall, 26.4 % reported use of solid fuel for both cooking and heating. Solid fuel users had 92 % higher personal and kitchen 24-h average PM2.5 exposure than clean fuel users. Similarly, they also had a greater increase (83 % vs 26 %) in personal and household PM2.5 from summer to winter, whereas community levels of PM2.5 were 2–4 times higher in winter across different fuel categories. Compared with clean fuel users, solid fuel users had markedly higher weighted annual average PM2.5 exposure at personal (78.2 [95 % CI 71.6–85.3] μg/m3 vs 41.6 [37.3–46.5] μg/m3), kitchen (102.4 [90.4–116.0] μg/m3 vs 52.3 [44.8–61.2] μg/m3) and living room (62.1 [57.3–67.3] μg/m3 vs 41.0 [37.1–45.3] μg/m3) microenvironments. There was a remarkable diurnal variability in PM2.5 exposure among the participants, with 5-min moving average from 10 μg/m3 to 700–1200 μg/m3 across different microenvironments. Personal PM2.5 was moderately correlated with living room (Spearman r: 0.64–0.66) and kitchen (0.52–0.59) levels, but only weakly correlated with community levels, especially in summer (0.15–0.34) and among solid fuel users (0.11–0.31).Conclusion: Solid fuel use for cooking and heating was associated with substantially higher personal and household PM2.5 exposure than clean fuel users. Household PM2.5 appeared a better proxy of personal exposure than community PM2.5. Fine particulate matter Exposure assessment Wearable sensor Solid fuels Cooking Heating Xia, Xi verfasserin aut Liu, Cong verfasserin aut Kan, Haidong verfasserin aut Doherty, Aiden verfasserin aut Yim, Steve Hung Lam verfasserin aut Wright, Neil verfasserin aut Kartsonaki, Christiana verfasserin aut Yang, Xiaoming verfasserin aut Stevens, Rebecca verfasserin aut Chang, Xiaoyu verfasserin aut Sun, Dianjianyi verfasserin aut Yu, Canqing verfasserin aut Lv, Jun verfasserin aut Li, Liming verfasserin aut Ho, Kin-Fai verfasserin aut Lam, Kin Bong Hubert verfasserin aut Chen, Zhengming verfasserin aut Enthalten in The science of the total environment Amsterdam [u.a.] : Elsevier Science, 1972 904 Online-Ressource (DE-627)306591456 (DE-600)1498726-0 (DE-576)081953178 1879-1026 nnns volume:904 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 904 |
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Enthalten in The science of the total environment 904 volume:904 |
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Umweltchemie Umwelttoxikologie Medizinische Ökologie |
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Fine particulate matter Exposure assessment Wearable sensor Solid fuels Cooking Heating |
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Chan, Ka Hung @@aut@@ Xia, Xi @@aut@@ Liu, Cong @@aut@@ Kan, Haidong @@aut@@ Doherty, Aiden @@aut@@ Yim, Steve Hung Lam @@aut@@ Wright, Neil @@aut@@ Kartsonaki, Christiana @@aut@@ Yang, Xiaoming @@aut@@ Stevens, Rebecca @@aut@@ Chang, Xiaoyu @@aut@@ Sun, Dianjianyi @@aut@@ Yu, Canqing @@aut@@ Lv, Jun @@aut@@ Li, Liming @@aut@@ Ho, Kin-Fai @@aut@@ Lam, Kin Bong Hubert @@aut@@ Chen, Zhengming @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV065351630</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240121093139.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231101s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.scitotenv.2023.166647</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV065351630</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0048-9697(23)05272-5</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">333.7</subfield><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">43.12</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">43.13</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.13</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Chan, Ka Hung</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Characterising personal, household, and community PM 2.5 exposure in one urban and two rural communities in China</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background: Cooking and heating in households contribute importantly to air pollution exposure worldwide. However, there is insufficient investigation of measured fine particulate matter (PM2.5) exposure levels, variability, seasonality, and inter-spatial dynamics associated with these behaviours.Methods: We undertook parallel measurements of personal, household (kitchen and living room), and community PM2.5 in summer (May–September 2017) and winter (November 2017-Janauary 2018) in 477 participants from one urban and two rural communities in China. After stringent data cleaning, there were 67,326–80,980 person-hours (ntotal = 441; nsummer = 384; nwinter = 364; 307 had repeated PM2.5 data in both seasons) of processed data per microenvironment. Age- and sex-adjusted geometric means of PM2.5 were calculated by key participant characteristics, overall and by season. Spearman correlation coefficients between PM2.5 levels across different microenvironments were computed.Findings: Overall, 26.4 % reported use of solid fuel for both cooking and heating. Solid fuel users had 92 % higher personal and kitchen 24-h average PM2.5 exposure than clean fuel users. Similarly, they also had a greater increase (83 % vs 26 %) in personal and household PM2.5 from summer to winter, whereas community levels of PM2.5 were 2–4 times higher in winter across different fuel categories. Compared with clean fuel users, solid fuel users had markedly higher weighted annual average PM2.5 exposure at personal (78.2 [95 % CI 71.6–85.3] μg/m3 vs 41.6 [37.3–46.5] μg/m3), kitchen (102.4 [90.4–116.0] μg/m3 vs 52.3 [44.8–61.2] μg/m3) and living room (62.1 [57.3–67.3] μg/m3 vs 41.0 [37.1–45.3] μg/m3) microenvironments. There was a remarkable diurnal variability in PM2.5 exposure among the participants, with 5-min moving average from 10 μg/m3 to 700–1200 μg/m3 across different microenvironments. Personal PM2.5 was moderately correlated with living room (Spearman r: 0.64–0.66) and kitchen (0.52–0.59) levels, but only weakly correlated with community levels, especially in summer (0.15–0.34) and among solid fuel users (0.11–0.31).Conclusion: Solid fuel use for cooking and heating was associated with substantially higher personal and household PM2.5 exposure than clean fuel users. 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Chan, Ka Hung |
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Chan, Ka Hung ddc 333.7 bkl 43.12 bkl 43.13 bkl 44.13 misc Fine particulate matter misc Exposure assessment misc Wearable sensor misc Solid fuels misc Cooking misc Heating Characterising personal, household, and community PM 2.5 exposure in one urban and two rural communities in China |
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333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Characterising personal, household, and community PM 2.5 exposure in one urban and two rural communities in China Fine particulate matter Exposure assessment Wearable sensor Solid fuels Cooking Heating |
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ddc 333.7 bkl 43.12 bkl 43.13 bkl 44.13 misc Fine particulate matter misc Exposure assessment misc Wearable sensor misc Solid fuels misc Cooking misc Heating |
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Characterising personal, household, and community PM 2.5 exposure in one urban and two rural communities in China |
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Characterising personal, household, and community PM 2.5 exposure in one urban and two rural communities in China |
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Chan, Ka Hung Xia, Xi Liu, Cong Kan, Haidong Doherty, Aiden Yim, Steve Hung Lam Wright, Neil Kartsonaki, Christiana Yang, Xiaoming Stevens, Rebecca Chang, Xiaoyu Sun, Dianjianyi Yu, Canqing Lv, Jun Li, Liming Ho, Kin-Fai Lam, Kin Bong Hubert Chen, Zhengming |
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characterising personal, household, and community pm 2.5 exposure in one urban and two rural communities in china |
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Characterising personal, household, and community PM 2.5 exposure in one urban and two rural communities in China |
| abstract |
Background: Cooking and heating in households contribute importantly to air pollution exposure worldwide. However, there is insufficient investigation of measured fine particulate matter (PM2.5) exposure levels, variability, seasonality, and inter-spatial dynamics associated with these behaviours.Methods: We undertook parallel measurements of personal, household (kitchen and living room), and community PM2.5 in summer (May–September 2017) and winter (November 2017-Janauary 2018) in 477 participants from one urban and two rural communities in China. After stringent data cleaning, there were 67,326–80,980 person-hours (ntotal = 441; nsummer = 384; nwinter = 364; 307 had repeated PM2.5 data in both seasons) of processed data per microenvironment. Age- and sex-adjusted geometric means of PM2.5 were calculated by key participant characteristics, overall and by season. Spearman correlation coefficients between PM2.5 levels across different microenvironments were computed.Findings: Overall, 26.4 % reported use of solid fuel for both cooking and heating. Solid fuel users had 92 % higher personal and kitchen 24-h average PM2.5 exposure than clean fuel users. Similarly, they also had a greater increase (83 % vs 26 %) in personal and household PM2.5 from summer to winter, whereas community levels of PM2.5 were 2–4 times higher in winter across different fuel categories. Compared with clean fuel users, solid fuel users had markedly higher weighted annual average PM2.5 exposure at personal (78.2 [95 % CI 71.6–85.3] μg/m3 vs 41.6 [37.3–46.5] μg/m3), kitchen (102.4 [90.4–116.0] μg/m3 vs 52.3 [44.8–61.2] μg/m3) and living room (62.1 [57.3–67.3] μg/m3 vs 41.0 [37.1–45.3] μg/m3) microenvironments. There was a remarkable diurnal variability in PM2.5 exposure among the participants, with 5-min moving average from 10 μg/m3 to 700–1200 μg/m3 across different microenvironments. Personal PM2.5 was moderately correlated with living room (Spearman r: 0.64–0.66) and kitchen (0.52–0.59) levels, but only weakly correlated with community levels, especially in summer (0.15–0.34) and among solid fuel users (0.11–0.31).Conclusion: Solid fuel use for cooking and heating was associated with substantially higher personal and household PM2.5 exposure than clean fuel users. Household PM2.5 appeared a better proxy of personal exposure than community PM2.5. |
| abstractGer |
Background: Cooking and heating in households contribute importantly to air pollution exposure worldwide. However, there is insufficient investigation of measured fine particulate matter (PM2.5) exposure levels, variability, seasonality, and inter-spatial dynamics associated with these behaviours.Methods: We undertook parallel measurements of personal, household (kitchen and living room), and community PM2.5 in summer (May–September 2017) and winter (November 2017-Janauary 2018) in 477 participants from one urban and two rural communities in China. After stringent data cleaning, there were 67,326–80,980 person-hours (ntotal = 441; nsummer = 384; nwinter = 364; 307 had repeated PM2.5 data in both seasons) of processed data per microenvironment. Age- and sex-adjusted geometric means of PM2.5 were calculated by key participant characteristics, overall and by season. Spearman correlation coefficients between PM2.5 levels across different microenvironments were computed.Findings: Overall, 26.4 % reported use of solid fuel for both cooking and heating. Solid fuel users had 92 % higher personal and kitchen 24-h average PM2.5 exposure than clean fuel users. Similarly, they also had a greater increase (83 % vs 26 %) in personal and household PM2.5 from summer to winter, whereas community levels of PM2.5 were 2–4 times higher in winter across different fuel categories. Compared with clean fuel users, solid fuel users had markedly higher weighted annual average PM2.5 exposure at personal (78.2 [95 % CI 71.6–85.3] μg/m3 vs 41.6 [37.3–46.5] μg/m3), kitchen (102.4 [90.4–116.0] μg/m3 vs 52.3 [44.8–61.2] μg/m3) and living room (62.1 [57.3–67.3] μg/m3 vs 41.0 [37.1–45.3] μg/m3) microenvironments. There was a remarkable diurnal variability in PM2.5 exposure among the participants, with 5-min moving average from 10 μg/m3 to 700–1200 μg/m3 across different microenvironments. Personal PM2.5 was moderately correlated with living room (Spearman r: 0.64–0.66) and kitchen (0.52–0.59) levels, but only weakly correlated with community levels, especially in summer (0.15–0.34) and among solid fuel users (0.11–0.31).Conclusion: Solid fuel use for cooking and heating was associated with substantially higher personal and household PM2.5 exposure than clean fuel users. Household PM2.5 appeared a better proxy of personal exposure than community PM2.5. |
| abstract_unstemmed |
Background: Cooking and heating in households contribute importantly to air pollution exposure worldwide. However, there is insufficient investigation of measured fine particulate matter (PM2.5) exposure levels, variability, seasonality, and inter-spatial dynamics associated with these behaviours.Methods: We undertook parallel measurements of personal, household (kitchen and living room), and community PM2.5 in summer (May–September 2017) and winter (November 2017-Janauary 2018) in 477 participants from one urban and two rural communities in China. After stringent data cleaning, there were 67,326–80,980 person-hours (ntotal = 441; nsummer = 384; nwinter = 364; 307 had repeated PM2.5 data in both seasons) of processed data per microenvironment. Age- and sex-adjusted geometric means of PM2.5 were calculated by key participant characteristics, overall and by season. Spearman correlation coefficients between PM2.5 levels across different microenvironments were computed.Findings: Overall, 26.4 % reported use of solid fuel for both cooking and heating. Solid fuel users had 92 % higher personal and kitchen 24-h average PM2.5 exposure than clean fuel users. Similarly, they also had a greater increase (83 % vs 26 %) in personal and household PM2.5 from summer to winter, whereas community levels of PM2.5 were 2–4 times higher in winter across different fuel categories. Compared with clean fuel users, solid fuel users had markedly higher weighted annual average PM2.5 exposure at personal (78.2 [95 % CI 71.6–85.3] μg/m3 vs 41.6 [37.3–46.5] μg/m3), kitchen (102.4 [90.4–116.0] μg/m3 vs 52.3 [44.8–61.2] μg/m3) and living room (62.1 [57.3–67.3] μg/m3 vs 41.0 [37.1–45.3] μg/m3) microenvironments. There was a remarkable diurnal variability in PM2.5 exposure among the participants, with 5-min moving average from 10 μg/m3 to 700–1200 μg/m3 across different microenvironments. Personal PM2.5 was moderately correlated with living room (Spearman r: 0.64–0.66) and kitchen (0.52–0.59) levels, but only weakly correlated with community levels, especially in summer (0.15–0.34) and among solid fuel users (0.11–0.31).Conclusion: Solid fuel use for cooking and heating was associated with substantially higher personal and household PM2.5 exposure than clean fuel users. Household PM2.5 appeared a better proxy of personal exposure than community PM2.5. |
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| title_short |
Characterising personal, household, and community PM 2.5 exposure in one urban and two rural communities in China |
| remote_bool |
true |
| author2 |
Xia, Xi Liu, Cong Kan, Haidong Doherty, Aiden Yim, Steve Hung Lam Wright, Neil Kartsonaki, Christiana Yang, Xiaoming Stevens, Rebecca Chang, Xiaoyu Sun, Dianjianyi Yu, Canqing Lv, Jun Li, Liming Ho, Kin-Fai Lam, Kin Bong Hubert Chen, Zhengming |
| author2Str |
Xia, Xi Liu, Cong Kan, Haidong Doherty, Aiden Yim, Steve Hung Lam Wright, Neil Kartsonaki, Christiana Yang, Xiaoming Stevens, Rebecca Chang, Xiaoyu Sun, Dianjianyi Yu, Canqing Lv, Jun Li, Liming Ho, Kin-Fai Lam, Kin Bong Hubert Chen, Zhengming |
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306591456 |
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c |
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false |
| hochschulschrift_bool |
false |
| doi_str |
10.1016/j.scitotenv.2023.166647 |
| up_date |
2024-07-06T22:43:49.215Z |
| _version_ |
1803871405178093568 |
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However, there is insufficient investigation of measured fine particulate matter (PM2.5) exposure levels, variability, seasonality, and inter-spatial dynamics associated with these behaviours.Methods: We undertook parallel measurements of personal, household (kitchen and living room), and community PM2.5 in summer (May–September 2017) and winter (November 2017-Janauary 2018) in 477 participants from one urban and two rural communities in China. After stringent data cleaning, there were 67,326–80,980 person-hours (ntotal = 441; nsummer = 384; nwinter = 364; 307 had repeated PM2.5 data in both seasons) of processed data per microenvironment. Age- and sex-adjusted geometric means of PM2.5 were calculated by key participant characteristics, overall and by season. Spearman correlation coefficients between PM2.5 levels across different microenvironments were computed.Findings: Overall, 26.4 % reported use of solid fuel for both cooking and heating. 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| score |
7.3980455 |