By Pratima Singh, Priyavrat Bhati, and Vignesh Prabhu
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.
The Central Pollution Control Board (CPCB) conducted SA and EI studies in six cities a decade ago; very little work has been done since. Though NCAP has identified these studies as important initial steps to prepare clean air plans, around 100 cities do not even have a basic emission inventory to identify the pollution sources. Notably, monitoring stations installed at multiple locations (residential, industrial, commercial, traffic junctions, etc.) in a city will aid in conducting SA studies. Since many of the non-attainment cities in India don’t have a sufficient number of monitoring stations installed, conducting credible SA studies will be a challenge.
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.
Satellite data to map rural areas
There is a commonly held misconception—arising possibly because of non-monitoring—that the pollution in urban areas is far higher than that in rural areas. While rural areas may have lower exposure to vehicular/industrial emissions, exposure to pollution from biomass cooking and other activities in rural areas is not understood adequately. Despite the rural population (65%) being much higher than the urban population (35%) in some Indian states (Bihar, Uttarakhand, Nagaland, Sikkim, Tripura, Assam, and Himachal Pradesh, for instance), the monitoring network in rural areas is highly inadequate, highlighting the lack of scientific rigour for understanding the pollution landscape. Moreover, scientific rigour needs to be accompanied by political urgency to improve the transparency of the data generation process. To compensate for the absence of monitoring stations in rural areas, many researchers rely on satellite measurements and meteorological parameters to estimate the PM levels. However, while satellite measurements are a good substitute for monitoring stations and bring increased accuracy, PM estimates obtained through these measurements can only be validated through monitoring stations on the ground.
One of the solutions to address the existing gaps in air pollution data is to employ a combination of monitoring methods—such as manual monitoring, real-time monitoring, satellite measurements, low-cost networks, or mobile measurements—with proper calibration. Further, monitoring in rural areas and tier 2 or 3 cities needs to be given as much, if not more, attention as that in urban areas or cities with million-plus populations.
The authors work in the area of Climate, Environment and Sustainability at the Center for Study of Science, Technology and Policy (CSTEP), a research-based think tank.