Assimilative capacity-based emission load management in a critically polluted industrial cluster.

UNLABELLED

In the present study, a modified approach was adopted to quantify the assimilative capacity (i.e., the maximum emission an area can take without violating the permissible pollutant standards) of a major industrial cluster (Manali, India) and to assess the effectiveness of adopted air pollution control measures at the region. Seasonal analysis of assimilative capacity was carried out corresponding to critical, high, medium, and low pollution levels to know the best and worst conditions for industrial operations. Bottom-up approach was employed to quantify sulfur dioxide (SO), nitrogen dioxide (NO), and particulate matter (aerodynamic diameter <10 μm; PM) emissions at a fine spatial resolution of 500 × 500 m in Manali industrial cluster. AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model), an U.S. Environmental Protection Agency (EPA) regulatory model, was used for estimating assimilative capacity. Results indicated that 22.8 tonnes/day of SO, 7.8 tonnes/day of NO, and 7.1 tonnes/day of PM were emitted from the industries of Manali. The estimated assimilative capacities for SO, NO, and PM were found to be 16.05, 17.36, and 19.78 tonnes/day, respectively. It was observed that the current SO emissions were exceeding the estimated safe load by 6.7 tonnes/day, whereas PM and NO were within the safe limits. Seasonal analysis of assimilative capacity showed that post-monsoon had the lowest load-carrying capacity, followed by winter, summer, and monsoon seasons, and the allowable SO emissions during post-monsoon and winter seasons were found to be 35% and 26% lower, respectively, when compared with monsoon season.

IMPLICATIONS

The authors present a modified approach for quantitative estimation of assimilative capacity of a critically polluted Indian industrial cluster. The authors developed a geo-coded fine-resolution PM, NO, and SO emission inventory for Manali industrial area and further quantitatively estimated its season-wise assimilative capacities corresponding to various pollution levels. This quantitative representation of assimilative capacity (in terms of emissions), when compared with routine qualitative representation, provides better data for quantifying carrying capacity of an area. This information helps policy makers and regulatory authorities to develop an effective mitigation plan for air pollution abatement.