QUANTIFICATION AND ANALYSIS OF PM2.5 AND ITS PRECURSORS USING GAIN MODEL IN PUNJAB, PAKISTAN

Abstract

The study for Punjab, Pakistan, used the GAINS South Asia model 2 to analyze emissions data and control measures. It relied on the "Final Report: Baseline08" scenario, which integrated legislative actions up to 2008. The GAIN Model's Scenario: PAKI_BAU_CPS_2021 was employed for emissions calculations, focusing on PM2.5 and its precursors. In 2020, major sources of PM2.5 emissions included residential combustion(42.4%), industrial processes(20.8%), and industrial combustion(14.6%). However, the modeled contribution of vehicles to PM2.5 was surprisingly low indicating that model has used low emission factors for vehicles in Punjab. Heavy-duty diesel vehicles (34.9%) were the primary sources of NOx emissions, with agriculture, light-duty vehicles, and power and heating plants also contributing 16% each to NOx emissions. SO2 emissions predominantly came from local coal, furnace oil, and diesel, primarily from power and heating plants (46.4%) and industrial combustion (30.7%). Agriculture was the dominant source of NH3 (91.7%).emissions. VOCs primarily originated from residential combustion (54.6%), light-duty vehicles (19.1%), and solvents. Black Carbon (BC) emissions had their major sources in residential combustion(58%). Actual data from Lahore also emphasized the significant vehicular contribution to PM2.5 emissions. The data suggests that OC and EC together form 39% of PM2.5 in busy areas of Lahore, highlighting vehicles as a major pollutant source. NH4NO3 and (NH4)2SO4 collectively contribute 37% to PM2.5, with NO3 at 13% surpassing SO4's 11%. Model data indicates SO2 at 482.8 Kt/yr and NOx at 661.5 Kt/yr. Despite NH3's significant 994 Kt/yr contribution, mainly from agriculture, NH4 levels are modest due to non-agricultural data sources.Notably, the GAIN model may have underestimated vehicle emission factors, with actual data indicating a potentially higher contribution from vehicles to various pollutants.