{"type":"survey","doc_desc":{"title":"WLD_2000_APWC_v01_M","idno":"DDI_WLD_2000_APWC_v02_M","prod_date":"2010-11-14","version_statement":{"version":"DDI Document  - Version 02 - (04\/21\/21)\n This version is identical to DDI_WLD_2000_APWC_v01_M but country field has been updated to capture all the countries covered by survey.\n\n Version 01 (September 2010)"}},"study_desc":{"title_statement":{"idno":"WLD_2000_APWC_v01_M","title":"Air Pollution in World Cities 2000","sub_title":"PM10 Concentrations","alt_title":"APWC 2000","identifiers":[{"type":"DOI","identifier":"https:\/\/doi.org\/10.48529\/xms0-9r35"}]},"authoring_entity":[{"name":"Kiran D. Pandey, David R. Wheeler, Uwe Deichmann, Kirk E. Hamilton, Bart Ostro and Katie Bolt","affiliation":""}],"production_statement":{"prod_date":"2010-11-14"},"distribution_statement":{"contact":[{"name":"Development Research Group","affiliation":"World Bank","email":"research@worldbank.org","uri":"http:\/\/go.worldbank.org\/B9W4QTDHR0"}]},"series_statement":{"series_name":"Macroeconomics - Indicators"},"study_info":{"abstract":"Polluted air is a major health hazard in developing countries.  Improvements in pollution monitoring and statistical techniques during the last several decades have steadily enhanced the ability to measure the health effects of air pollution. Current methods can detect significant increases in the incidence of cardiopulmonary and respiratory diseases, coughing, bronchitis, and lung cancer, as well as premature deaths from these diseases resulting from elevated concentrations of ambient Particulate Matter (Holgate 1999). \n\nScarce public resources have limited the monitoring of atmospheric particulate matter (PM) concentrations in developing countries, despite their large potential health effects.  As a result, policymakers in many developing countries remain uncertain about the exposure of their residents to PM air pollution.  The Global Model of Ambient Particulates (GMAPS) is an attempt to bridge this information gap through an econometrically estimated model for predicting PM levels in world cities (Pandey et al. forthcoming).\n\nThe estimation model is based on the latest available monitored PM pollution data from the World Health Organization, supplemented by data from other reliable sources.  The current model can be used to estimate PM levels in urban residential areas and non-residential pollution hotspots.  The results of the model are used to project annual average ambient PM concentrations for residential and non-residential areas in 3,226 world cities with populations larger than 100,000, as well as national capitals. \n\nThe study finds wide, systematic variations in ambient PM concentrations, both across world cities and over time.  PM concentrations have risen at a slower rate than total emissions. Overall emission levels have been rising, especially for poorer countries, at nearly 6 percent per year.  PM concentrations have not increased by as much, due to improvements in technology and structural shifts in the world economy.  Additionally, within-country variations in PM levels can diverge greatly (by a factor of 5 in some cases), because of the direct and indirect effects of geo-climatic factors.\n\nThe primary determinants of PM concentrations are the scale and composition of economic activity, population, the energy mix, the strength of local pollution regulation, and geographic and atmospheric conditions that affect pollutant dispersion in the atmosphere.","time_periods":[{"start":"1999","end":"2000","cycle":""}],"coll_dates":[{"start":"1999","end":"1999","cycle":"Pollution data"},{"start":"2000","end":"2000","cycle":"Population data"}],"nation":[{"name":"Afghanistan","abbreviation":"AFG"},{"name":"Angola","abbreviation":"AGO"},{"name":"Albania","abbreviation":"ALB"},{"name":"Andorra","abbreviation":"AND"},{"name":"Netherlands Antilles","abbreviation":"ANT"},{"name":"United Arab Emirates","abbreviation":"ARE"},{"name":"Argentina","abbreviation":"ARG"},{"name":"Armenia","abbreviation":"ARM"},{"name":"Antigua and Barbuda","abbreviation":"ATG"},{"name":"Australia","abbreviation":"AUS"},{"name":"Austria","abbreviation":"AUT"},{"name":"Azerbaijan","abbreviation":"AZE"},{"name":"Burundi","abbreviation":"BDI"},{"name":"Belgium","abbreviation":"BEL"},{"name":"Benin","abbreviation":"BEN"},{"name":"Burkina Faso","abbreviation":"BFA"},{"name":"Bulgaria","abbreviation":"BGR"},{"name":"Bahrain","abbreviation":"BHR"},{"name":"Bosnia and Herzegovina","abbreviation":"BIH"},{"name":"Bolivia","abbreviation":"BOL"},{"name":"Brazil","abbreviation":"BRA"},{"name":"Barbados","abbreviation":"BRB"},{"name":"Bhutan","abbreviation":"BTN"},{"name":"Central African Republic","abbreviation":"CAF"},{"name":"Canada","abbreviation":"CAN"},{"name":"Switzerland","abbreviation":"CHE"},{"name":"Chile","abbreviation":"CHL"},{"name":"China","abbreviation":"CHN"},{"name":"Cameroon","abbreviation":"CMR"},{"name":"Congo, Rep.","abbreviation":"COG"},{"name":"Congo, Rep.","abbreviation":"COG"},{"name":"Comoros","abbreviation":"COM"},{"name":"Costa Rica","abbreviation":"CRI"},{"name":"Cuba","abbreviation":"CUB"},{"name":"Cyprus","abbreviation":"CYP"},{"name":"Germany","abbreviation":"DEU"},{"name":"Dominica","abbreviation":"DMA"},{"name":"Denmark","abbreviation":"DNK"},{"name":"Dominican Republic","abbreviation":"DOM"},{"name":"Algeria","abbreviation":"DZA"},{"name":"Ecuador","abbreviation":"ECU"},{"name":"Egypt, Arab Rep.","abbreviation":"EGY"},{"name":"Spain","abbreviation":"ESP"},{"name":"Estonia","abbreviation":"EST"},{"name":"Ethiopia","abbreviation":"ETH"},{"name":"Finland","abbreviation":"FIN"},{"name":"Fiji","abbreviation":"FJI"},{"name":"France","abbreviation":"FRA"},{"name":"Gabon","abbreviation":"GAB"},{"name":"United Kingdom","abbreviation":"GBR"},{"name":"Georgia","abbreviation":"GEO"},{"name":"Ghana","abbreviation":"GHA"},{"name":"Guinea","abbreviation":"GIN"},{"name":"Guinea-Bissau","abbreviation":"GNB"},{"name":"Greece","abbreviation":"GRC"},{"name":"Grenada","abbreviation":"GRD"},{"name":"Guatemala","abbreviation":"GTM"},{"name":"Guyana","abbreviation":"GUY"},{"name":"Hong Kong SAR, China","abbreviation":"HKG"},{"name":"Honduras","abbreviation":"HND"},{"name":"Croatia","abbreviation":"HRV"},{"name":"Haiti","abbreviation":"HTI"},{"name":"Hungary","abbreviation":"HUN"},{"name":"Indonesia","abbreviation":"IDN"},{"name":"India","abbreviation":"IND"},{"name":"Ireland","abbreviation":"IRL"},{"name":"Iraq","abbreviation":"IRQ"},{"name":"Iceland","abbreviation":"ISL"},{"name":"Israel","abbreviation":"ISR"},{"name":"Italy","abbreviation":"ITA"},{"name":"Jamaica","abbreviation":"JAM"},{"name":"Jordan","abbreviation":"JOR"},{"name":"Japan","abbreviation":"JPN"},{"name":"Kazakhstan","abbreviation":"KAZ"},{"name":"Kenya","abbreviation":"KEN"},{"name":"Cambodia","abbreviation":"KHM"},{"name":"St. Kitts and Nevis","abbreviation":"KNA"},{"name":"Korea, Rep.","abbreviation":"KOR"},{"name":"Kuwait","abbreviation":"KWT"},{"name":"Lao PDR","abbreviation":"LAO"},{"name":"Liberia","abbreviation":"LBR"},{"name":"St. Lucia","abbreviation":"LCA"},{"name":"Liechtenstein","abbreviation":"LIE"},{"name":"Sri Lanka","abbreviation":"LKA"},{"name":"Lesotho","abbreviation":"LSO"},{"name":"Lithuania","abbreviation":"LTU"},{"name":"Latvia","abbreviation":"LVA"},{"name":"Macao SAR, China","abbreviation":"MAC"},{"name":"Morocco","abbreviation":"MAR"},{"name":"Moldova","abbreviation":"MDA"},{"name":"Madagascar","abbreviation":"MDG"},{"name":"Maldives","abbreviation":"MDV"},{"name":"Mexico","abbreviation":"MEX"},{"name":"North Macedonia","abbreviation":"MKD"},{"name":"Mali","abbreviation":"MLI"},{"name":"Myanmar","abbreviation":"MMR"},{"name":"Montenegro","abbreviation":"MNE"},{"name":"Mongolia","abbreviation":"MNG"},{"name":"Mozambique","abbreviation":"MOZ"},{"name":"Mauritania","abbreviation":"MRT"},{"name":"Malawi","abbreviation":"MWI"},{"name":"Malaysia","abbreviation":"MYS"},{"name":"Namibia","abbreviation":"NAM"},{"name":"New Caledonia","abbreviation":"NCL"},{"name":"Niger","abbreviation":"NER"},{"name":"Nigeria","abbreviation":"NGA"},{"name":"Nicaragua","abbreviation":"NIC"},{"name":"Netherlands","abbreviation":"NLD"},{"name":"New Zealand","abbreviation":"NZL"},{"name":"Oman","abbreviation":"OMN"},{"name":"Pakistan","abbreviation":"PAK"},{"name":"Panama","abbreviation":"PAN"},{"name":"Peru","abbreviation":"PER"},{"name":"Philippines","abbreviation":"PHL"},{"name":"Papua New Guinea","abbreviation":"PNG"},{"name":"Poland","abbreviation":"POL"},{"name":"Puerto Rico","abbreviation":"PRI"},{"name":"Portugal","abbreviation":"PRT"},{"name":"Paraguay","abbreviation":"PRY"},{"name":"Qatar","abbreviation":"QAT"},{"name":"Romania","abbreviation":"ROU"},{"name":"Russian Federation","abbreviation":"RUS"},{"name":"Rwanda","abbreviation":"RWA"},{"name":"Saudi Arabia","abbreviation":"SAU"},{"name":"Sudan","abbreviation":"SDN"},{"name":"Senegal","abbreviation":"SEN"},{"name":"Singapore","abbreviation":"SGP"},{"name":"Solomon Islands","abbreviation":"SLB"},{"name":"Sierra Leone","abbreviation":"SLE"},{"name":"El Salvador","abbreviation":"SLV"},{"name":"Somalia","abbreviation":"SOM"},{"name":"Serbia","abbreviation":"SRB"},{"name":"Sao Tome and Principe","abbreviation":"STP"},{"name":"Suriname","abbreviation":"SUR"},{"name":"Slovak Republic","abbreviation":"SVK"},{"name":"Slovenia","abbreviation":"SVN"},{"name":"Sweden","abbreviation":"SWE"},{"name":"Eswatini","abbreviation":"SWZ"},{"name":"Syrian Arab Republic","abbreviation":"SYR"},{"name":"Togo","abbreviation":"TGO"},{"name":"Thailand","abbreviation":"THA"},{"name":"Tajikistan","abbreviation":"TJK"},{"name":"Turkmenistan","abbreviation":"TKM"},{"name":"Trinidad and 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Rep.","abbreviation":"YUG"}],"geog_coverage":"The database covers the following countries:\nAfghanistan\t\nAlbania\t\nAlgeria\t\nAndorra\t\nAngola\t\nAntigua and Barbuda\t\nArgentina\t\nArmenia\t\nAustralia\t\nAustria\t\nAzerbaijan\t\nBahamas, The\t\nBahrain\t\nBangladesh\t\nBarbados\t\nBelarus\t\nBelgium\t\nBelize\t\nBenin\t\nBhutan\t\nBolivia\t\nBosnia and Herzegovina\t\nBrazil\t\nBrunei\t\nBulgaria\t\nBurkina Faso\t\nBurundi\t\nCambodia\t\nCameroon\t\nCanada\t\nCayman Islands\t\nCentral African Republic\t\nChad\t\nChile\t\nChina\t\nColombia\t\nComoros\t\nCongo, Dem. Rep.\t\nCongo, Rep.\t\nCosta Rica\t\nCote d'Ivoire\t\nCroatia\t\nCuba\t\nCyprus\t\nCzech Republic\t\nDenmark\t\nDominica\t\nDominican Republic\t\nEcuador\t\nEgypt, Arab Rep.\t\nEl Salvador\t\nEritrea\t\nEstonia\t\nEthiopia\t\nFaeroe Islands\t\nFiji\t\nFinland\t\nFrance\t\nGabon\t\nGambia, The\t\nGeorgia\t\nGermany\t\nGhana\t\nGreece\t\nGrenada\t\nGuatemala\t\nGuinea\t\nGuinea-Bissau\t\nGuyana\t\nHaiti\t\nHonduras\t\nHong Kong, China\t\nHungary\t\nIceland\t\nIndia\t\nIndonesia\t\nIran, Islamic Rep.\t\nIraq\t\nIreland\t\nIsrael\t\nItaly\t\nJamaica\t\nJapan\t\nJordan\t\nKazakhstan\t\nKenya\t\nKorea, Dem. Rep.\t\nKorea, Rep.\t\nKuwait\t\nKyrgyz Republic\t\nLao PDR\t\nLatvia\t\nLebanon\t\nLesotho\t\nLiberia\t\nLiechtenstein\t\nLithuania\t\nLuxembourg\t\nMacao, China\t\nMacedonia, FYR\t\nMadagascar\t\nMalawi\t\nMalaysia\t\nMaldives\t\nMali\t\nMauritania\t\nMexico\t\nMoldova\t\nMongolia\t\nMorocco\t\nMozambique\t\nMyanmar\t\nNamibia\t\nNepal\t\nNetherlands\t\nNetherlands Antilles\t\nNew Caledonia\t\nNew Zealand\t\nNicaragua\t\nNiger\t\nNigeria\t\nNorway\t\nOman\t\nPakistan\t\nPanama\t\nPapua New Guinea\t\nParaguay\t\nPeru\t\nPhilippines\t\nPoland\t\nPortugal\t\nPuerto Rico\t\nQatar\t\nRomania\t\nRussian Federation\t\nRwanda\t\nSao Tome and Principe\t\nSaudi Arabia\t\nSenegal\t\nSierra Leone\t\nSingapore\t\nSlovak Republic\t\nSlovenia\t\nSolomon Islands\t\nSomalia\t\nSouth Africa\t\nSpain\t\nSri Lanka\t\nSt. Kitts and Nevis\t\nSt. Lucia\t\nSt. Vincent and the Grenadines\t\nSudan\t\nSuriname\t\nSwaziland\t\nSweden\t\nSwitzerland\t\nSyrian Arab Republic\t\nTajikistan\t\nTanzania\t\nThailand\t\nTogo\t\nTrinidad and Tobago\t\nTunisia\t\nTurkey\t\nTurkmenistan\t\nUganda\t\nUkraine\t\nUnited Arab Emirates\t\nUnited Kingdom\t\nUnited States\t\nUruguay\t\nUzbekistan\t\nVanuatu\t\nVenezuela, RB\t\nVietnam\t\nVirgin Islands (U.S.)\t\nYemen, Rep.\t\nYugoslavia, FR (Serbia\/Montenegro)\t\nZambia\t\nZimbabwe","data_kind":"Observation data\/ratings [obs]","notes":"The dataset includes the followings:\n- Urban population\n- PM10 concentration level"},"method":{"data_collection":{"coll_mode":"Other [oth]"}},"data_access":{"dataset_use":{"cit_req":"Use of the dataset must be acknowledged using a citation which would include:\n- the Identification of the Primary Investigator\n- the title of the survey (including acronym and year of implementation)\n- the survey reference number\n- the source and date of download\n\nExample:\n\nKiran Dev Pandey et al. Air Pollution in World Cities (APWC) 2000. Ref. WLD_2000_APWC_v01_M. Dataset downloaded from http:\/\/microdata.worldbank.org on [date].","disclaimer":"The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses."}}},"tags":[{"tag":"DOI"}],"schematype":"survey","data_files":[{"file_id":"F1","file_name":"AP_city","description":"Air Pollution Data by City\nUrban population weighted average PM10 concentrations (micro grams per cubic meter) in residential areas of cities larger than 100,000","case_count":3226,"var_count":6,"producer":null,"data_checks":null,"version":null,"notes":null,"metadata":null},{"file_id":"F2","file_name":"AP_country","description":"Air Pollution Data by Country\nUrban population weighted average PM10 concentrations (micro grams per cubic meter) in residential areas of cities larger than 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