Measuring Particulate Matter To Solve Air Pollution Problem

Posted by Gyane on February 19th, 2021

The “scary” air pollution situation in India makes it imperative to gather extensive, credible data on pollution and its sources at a fairly granular level—for every square kilometre of most mid- to large-sized cities, as well as for vast exurban and rural areas. But don’t we already have enough information on the pollution levels in cities and also the main culprits? Shouldn’t pollution control plans, therefore, be self-evident? The answer to both these questions is a resounding no. As cities and state governments face pressure from citizens to take urgent action, extremely poor “Clean Air” plans are being made and implemented based on inadequate data in dozens of cities across the country.

The National Clean Air Programme (NCAP; launched in 2019)—now renamed National Clear Air Mission—aims to reduce the particulate matter (PM10 and PM2.5) concentrations in the air by 20–30% by 2024. In the first stage, NCAP identified 122 “non-attainment” cities where air pollution exceeded national standards. While this number might seem large, many more cities and rural areas with unsafe pollution levels haven’t been identified simply because they lack air quality monitoring stations to measure the pollution.

Lack of data

In India cities, only 804 PM10 and 309 PM2.5 monitoring stations are currently installed; these numbers are not enough to properly understand the spatial and temporal variations in pollution in most of these cities. Of the 122 non-attainment cities, 58 do not have any PM2.5 monitoring station to track the deadlier smaller sized particles. Many states, including Haryana and Punjab, do not have any PM2.5 monitoring stations; in fact, most of the eastern states of the country lack a PM2.5 monitoring station. To ascertain whether we have achieved NCAP goals will require knowing precisely the level and spread of air pollution.

Data about ambient levels of particulate matter, gathered by monitoring stations, will help in assessing the impact on human health. This data will also be essential to validate scientific studies such as those on source apportionment (SA) and emission inventory (EI), which will help in understanding the spatial and temporal patterns of air pollution. Further, these studies will help in identifying the primary pollution sources, formulating strategies, and prioritising actions to improve air quality. Finally, data monitoring will help in determining whether the policies selected for implementation enable any meaningful impact.

Low-cost sensor network

There is also an inequitable facet to the installed air quality monitors. Many states have advanced continuous monitoring stations, which provide real-time estimates of PM levels. However, a third of the continuous monitoring stations are located in just six cities—Delhi, Mumbai, Kolkata, Chennai, Hyderabad, and Bengaluru. Data needs to be gathered for a far wider geographical area that includes tier 2 and 3 cities, as well as rural areas. An appropriate strategy might be (1) the setting up of a network of low-cost sensors, say across the Indo-Gangetic Plain and other regions, to obtain a comprehensive understanding of air pollution levels and to identify local hotspots, and (2) the inclusion of tailored regional strategies that would be more effective than city-specific plans in enabling targeted action steps. Low-cost sensors are an effective tool to identify PM levels and gaseous pollutants; however, uncertainties in the data generated by these sensors make it difficult to use the data for compliance.