Appendices Table S1: List of countries in Eora. Country/Territory Acronym Country/Territory Acronym 1 Afghanistan AFG 96 Lesotho LSO 2 Albania ALB 97 Liberia LBR 3 Algeria DZA 98 Libya LBY 4 Andorra AND 99 Liechtenstein LIE 5 Angola AGO 100 Lithuania LTU 6 Antigua ATG 101 Luxembourg LUX 7 Argentina ARG 102 Macao SAR MAC 8 Armenia ARM 103 Madagascar MDG 9 Aruba ABW 104 Malawi MWI 10 Australia AUS 105 Malaysia MYS 11 Austria AUT 106 Maldives MDV 12 Azerbaijan AZE 107 Mali MLI 13 Bahamas BHS 108 Malta MLT 14 Bahrain BHR 109 Mauritania MRT 15 Bangladesh BGD 110 Mauritius MUS 16 Barbados BRB 111 Mexico MEX 17 Belarus BLR 112 Monaco MCO 18 Belgium BEL 113 Mongolia MNG 19 Belize BLZ 114 Montenegro MNE 20 Benin BEN 115 Morocco MAR 21 Bermuda BMU 116 Mozambique MOZ 22 Bhutan BTN 117 Myanmar MMR 23 Bolivia BOL 118 Namibia NAM 24 Bosnia and Herzegovina BIH 119 Nepal NPL 25 Botswana BWA 120 Netherlands NLD 26 Brazil BRA 121 Netherlands Antilles ANT 27 British Virgin Islands VGB 122 New Caledonia NCL 28 Brunei BRN 123 New Zealand NZL 29 Bulgaria BGR 124 Nicaragua NIC 30 Burkina Faso BFA 125 Niger NER 31 Burundi BDI 126 Nigeria NGA 32 Cambodia KHM 127 Norway NOR 33 Cameroon CMR 128 Gaza PSE 34 Canada CAN 129 Oman OMN 35 Cape CPV 130 Pakistan PAK 36 Cayman Islands CYM 131 Panama PAN 37 Central African Republic CAF 132 Papua New Guinea PNG 38 Chad TCD 133 Paraguay PRY 39 Chile CHL 134 Peru PER 40 China CHN 135 Philippines PHL 41 Colombia COL 136 Poland POL 42 Congo COG 137 Portugal PRT 43 Costa Rica CRI 138 Qatar QAT 44 Croatia HRV 139 South Korea KOR 45 Cuba CUB 140 Moldova MDA 46 Cyprus CYP 141 Romania ROU 47 Czech Republic CZE 142 Russia RUS
Example of RR values (Tim, Kyle, Imel, & James, 2004) Table S2-1. Relative risks for asthma in males and females, by occupation S2-2 Relative risk correspondence from occupation to sector for asthma Occupation in literature Sector in Eora Male Female 1 Agricultural Agriculture 1.41 1.41 2 Agricultural Fishing 1.41 1.41 3 Mining Mining and Quarrying 1.95 1 4 Manufacturing Food & Beverages 1.56 1.33 5 Manufacturing Textiles and Wearing Apparel 1.56 1.33 6 Manufacturing Wood and Paper 1.56 1.33 7 Manufacturing Petroleum, Chemical and Mineral Products 1.56 1.33 8 Manufacturing Metal Products 1.56 1.33 9 Manufacturing Electrical and Machinery 1.56 1.33 10 Manufacturing Transport Equipment 1.56 1.33 11 Manufacturing Other Manufacturing 1.56 1.33 12 Technical Recycling 1.05 1.06 13 Technical Electricity, Gas and Water 1.05 1.06 14 Technical Construction 1.05 1.06 15 Technical Maintenance and Repair 1.05 1.06 16 Sales Wholesale 1.14 1.13 17 Sales Retail 1.14 1.13 18 Services Hotels and Restaurants 1.53 1.41 19 Transportation Transport 1.31 1.22 20 Services Post and Telecommunications 1.53 1.41 21 Background Financial Intermediation and Business Activities 1 1 22 Administration Public Administration 1 1 23 Background Education and Health 1 1 24 Services Private Households 1.53 1.41 25 Background Others 1 1 Table S3. DALYs due to occupational asthma for all WHO subregions, by sex, 2000
Explanation S2: Reason of selecting PM10 but not PM2.5 as the airborne particulate indicator The two commonly used indicators to describe the relevance between PM and human health are PM10 and PM2.5. PM10, also called coarse particles, are the particles with a diameter of 10 micrometers or less, and PM2.5, also called fine particles, are the particles with a diameter of 2.5 micrometers or less. Admittedly, compared to PM10, PM2.5 has more harmful effects on human health, probably because of the high risks caused by ultra-fine particles (PM0.1). PM10 is primarily used due to data availability. In addition, numerous studies showed that there is strong correlation between PM10 and PM2.5. When there are only available data for PM10, different coefficients are used to estimate PM2.5 concentrations. For example, in the report of national PM2.5 provided by WHO, national conversion factors (PM2.5/PM10 ratio) were by country, and then used to estimate PM2.5 concentrations from PM10. In a worldwide scale, there are still many countries only providing monitored PM10 parameters, so that quite a lot of values of PM2.5 are originated from original PM10 data. Furthermore, varying by locations, the constitutions of PM2.5 in PM10 are different, which suggests the estimation process of PM2.5 from PM10 is complicated and depends on situations. Furthermore, some case studies proved that the uncertainties of PM2.5 data are inherently larger than that of PM10 data, meaning that PM10 is a more stable indicator. In summary, because of the data availability and certainty, the indicator PM10 is more proper than PM2.5 to be the indicator of air pollution in our research.