Importance of source apportionment modeling in air pollution abatement policy

Abstract

Air pollution problems, particularly in the emerging economies, are endangering the health and welfare of people. Both ambient and indoor air pollution levels have reached an alarming stage in the developing countries. Major sources of ambient air pollution are industries, power plants, and motor vehicles emitting the high levels of Oxides of Sulfur (SOx), Oxides of Nitrogen (NOx), Carbon Monoxide (CO), Suspended Particulate Matter (SPM), Hydro Carbons (HC), and numerous other pollutants. On the other hand, high level of indoor air pollution is the result of low quality fuels such as wood, coal, and kerosene, etc., that are being used in rural and urban poor households. Several studies have shown that the levels of SPM, NOx, and HC are resulting in higher incidences of respiratory diseases like tuberculosis, cardiovascular diseases, and asthma. In addition to the health hazards, air pollution also has several other detrimental effects such as reduction in visibility, spoiling of buildings, damage to material and machines, and ill effects on vegetation and animals (Hopke 1988; Okamoto et al. 1990; Sharma and Patil 1992, 1994; Sharma 2007). While the sources of the most gaseous pollutants are well defined, it is difficult to identify the origin of particulate pollutants. Even if it is possible to identify the major sources of such pollutants, qualitatively, their quantitative contributions may not be ascertained by the policy makers. As the decision makers are not sure of the share of pollution from various contributing sources in an area, they can not use any policy measure focusing on a particular source or a group of sources. Thus, implementation of a pollution abatement policy without identifying the real culprit, i.e., the major pollution source(s) in an area becomes difficult. In such a situation, the site specific source apportionment techniques could act as the management and decisionmaking tools. Source Apportionment Modeling (SAM) techniques are basically statistical methods, which essentially use a chemical mass balance equation. The main assumption of these techniques is that the pollutants do not transform, physically or chemically, during their transport from the source to the receptor. For example, the amount of total lead (Pb) arriving at a receptor will be the linear sum of the lead emitted by all sources of lead surrounding the receptor, say, lead from automobiles, lead from an incinerator, etc.,