Air Pollution in World Cities 2000 - Afghanistan, Angola, Albania...and 158 more

Abstract
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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).

Scarce 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).

The 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.

The 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.

The 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.

Geographic coverage
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The database covers the following countries:
Afghanistan
Albania
Algeria
Andorra
Angola
Antigua and Barbuda
Argentina
Armenia
Australia
Austria
Azerbaijan
Bahamas, The
Bahrain
Bangladesh
Barbados
Belarus
Belgium
Belize
Benin
Bhutan
Bolivia
Bosnia and Herzegovina
Brazil
Brunei
Bulgaria
Burkina Faso
Burundi
Cambodia
Cameroon
Canada
Cayman Islands
Central African Republic
Chad
Chile
China
Colombia
Comoros
Congo, Dem. Rep.
Congo, Rep.
Costa Rica
Cote d'Ivoire
Croatia
Cuba
Cyprus
Czech Republic
Denmark
Dominica
Dominican Republic
Ecuador
Egypt, Arab Rep.
El Salvador
Eritrea
Estonia
Ethiopia
Faeroe Islands
Fiji
Finland
France
Gabon
Gambia, The
Georgia
Germany
Ghana
Greece
Grenada
Guatemala
Guinea
Guinea-Bissau
Guyana
Haiti
Honduras
Hong Kong, China
Hungary
Iceland
India
Indonesia
Iran, Islamic Rep.
Iraq
Ireland
Israel
Italy
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Korea, Dem. Rep.
Korea, Rep.
Kuwait
Kyrgyz Republic
Lao PDR
Latvia
Lebanon
Lesotho
Liberia
Liechtenstein
Lithuania
Luxembourg
Macao, China
Macedonia, FYR
Madagascar
Malawi
Malaysia
Maldives
Mali
Mauritania
Mexico
Moldova
Mongolia
Morocco
Mozambique
Myanmar
Namibia
Nepal
Netherlands
Netherlands Antilles
New Caledonia
New Zealand
Nicaragua
Niger
Nigeria
Norway
Oman
Pakistan
Panama
Papua New Guinea
Paraguay
Peru
Philippines
Poland
Portugal
Puerto Rico
Qatar
Romania
Russian Federation
Rwanda
Sao Tome and Principe
Saudi Arabia
Senegal
Sierra Leone
Singapore
Slovak Republic
Slovenia
Solomon Islands
Somalia
South Africa
Spain
Sri Lanka
St. Kitts and Nevis
St. Lucia
St. Vincent and the Grenadines
Sudan
Suriname
Swaziland
Sweden
Switzerland
Syrian Arab Republic
Tajikistan
Tanzania
Thailand
Togo
Trinidad and Tobago
Tunisia
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Vanuatu
Venezuela, RB
Vietnam
Virgin Islands (U.S.)
Yemen, Rep.
Yugoslavia, FR (Serbia/Montenegro)
Zambia
Zimbabwe

Kind of data
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Observation data/ratings [obs]

Mode of data collection
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Other [oth]

摘要
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受污染的空气是发展中国家的一种主要健康危害。在过去的几十年中，污染监测和统计技术的改进不断增强了衡量空气污染健康效应的能力。目前的方法可以检测到心血管和呼吸系统疾病、咳嗽、支气管炎和肺癌发病率的显著增加，以及由于环境颗粒物（PM）浓度升高导致的这些疾病的过早死亡（Holgate 1999）。
公共资源的匮乏限制了发展中国家对大气颗粒物（PM）浓度的监测，尽管其潜在的健康影响巨大。因此，许多发展中国家的政策制定者对其居民接触PM空气污染的程度仍然感到不确定。全球环境颗粒物模型（GMAPS）旨在通过一个计量经济学估计模型来弥合这一信息差距，以预测世界各大城市的PM水平（Pandey等人，即将发表）。
估计模型基于世界卫生组织提供的最新可用的PM污染监测数据，并辅以其他可靠来源的数据。当前的模型可用于估算城市住宅区和非住宅污染热点地区的PM水平。该模型的结果被用于预测3,226个人口超过10万的世界城市及各国首都的住宅区和非住宅区的年度平均环境PM浓度。
研究发现，环境PM浓度在世界各大城市和时间上存在广泛的系统性差异。PM浓度的增长速度低于总排放量。总体排放水平一直在上升，特别是对于较贫穷的国家，每年增长近6%。由于技术进步和世界经济结构的转变，PM浓度并未以相同的速度增长。此外，由于地理气候因素的直接和间接影响，国内PM水平的差异可能非常大（在某些情况下达到5倍）。
PM浓度的决定因素主要是经济活动的规模和构成、人口、能源结构、地方污染监管的强度以及影响大气中污染物扩散的地理和大气条件。
地理覆盖范围
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数据库涵盖了以下国家：
阿富汗、阿尔巴尼亚、阿尔及利亚、安道尔、安哥拉、安提瓜和巴布达、阿根廷、亚美尼亚、澳大利亚、奥地利、阿塞拜疆、巴哈马、巴林、孟加拉国、巴巴多斯、白俄罗斯、比利时、伯利兹、贝宁、不丹、玻利维亚、波斯尼亚和黑塞哥维那、巴西、文莱、保加利亚、布基纳法索、布隆迪、柬埔寨、喀麦隆、加拿大、开曼群岛、中非共和国、乍得、智利、中国、哥伦比亚、科摩罗、刚果（民主共和国）、刚果（共和国）、哥斯达黎加、科特迪瓦、克罗地亚、古巴、塞浦路斯、捷克共和国、丹麦、多米尼加、多米尼加共和国、厄瓜多尔、埃及、萨尔瓦多、厄立特里亚、爱沙尼亚、埃塞俄比亚、法罗群岛、斐济、芬兰、法国、加蓬、冈比亚、格鲁吉亚、德国、加纳、希腊、格林纳达、危地马拉、几内亚、几内亚比绍、圭亚那、海地、洪都拉斯、香港、匈牙利、冰岛、印度、印度尼西亚、伊朗、伊拉克、爱尔兰、以色列、意大利、牙买加、日本、约旦、哈萨克斯坦、肯尼亚、朝鲜（民主共和国）、朝鲜（共和国）、科威特、吉尔吉斯共和国、老挝人民民主共和国、拉脱维亚、黎巴嫩、莱索托、利比里亚、列支敦士登、立陶宛、卢森堡、澳门、马其顿、马达加斯加、马拉维、马来西亚、马尔代夫、马里、毛里塔尼亚、墨西哥、摩尔多瓦、蒙古、摩洛哥、莫桑比克、缅甸、纳米比亚、尼泊尔、荷兰、荷属安的列斯群岛、新喀里多尼亚、新西兰、尼加拉瓜、尼日尔、尼日利亚、挪威、阿曼、巴基斯坦、巴拿马、巴布亚新几内亚、巴拉圭、秘鲁、菲律宾、波兰、葡萄牙、波多黎各、卡塔尔、罗马尼亚、俄罗斯联邦、卢旺达、圣多美和普林西比、沙特阿拉伯、塞内加尔、塞拉利昂、新加坡、斯洛伐克共和国、斯洛文尼亚、所罗门群岛、索马里、南非、西班牙、斯里兰卡、圣基茨和尼维斯、圣卢西亚、圣文森特和格林纳丁斯、苏丹、苏里南、斯威士兰、瑞典、瑞士、叙利亚阿拉伯共和国、塔吉克斯坦、坦桑尼亚、泰国、多哥、特立尼达和多巴哥、突尼斯、土耳其、土库曼斯坦、乌干达、乌克兰、阿拉伯联合酋长国、大不列颠及北爱尔兰联合王国、美国、乌拉圭、乌兹别克斯坦、瓦努阿图、委内瑞拉、越南、美属维尔京群岛、也门、南斯拉夫联邦共和国（塞尔维亚/黑山）、赞比亚、津巴布韦。
数据类型
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观测数据/评级 [obs]
数据收集方式
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其他 [oth]