The team, led by Aniket Chavan together with Dilip H. Lataye of the Department of Civil Engineering, analyzed data collected throughout 2023 at a Continuous Ambient Air Quality Monitoring Station in Nagpur, a rapidly expanding Tier-2 city in central India. Rather than relying on a single metric, the researchers examined eight major pollutants simultaneously: particulate matter of two size fractions, PM10 and PM2.5, along with sulfur dioxide, carbon monoxide, ozone, nitrogen dioxide, ammonia, and benzene. These were paired with four meteorological parameters: temperature, relative humidity, wind speed, and rainfall. The analysis was carried out in the R programming environment using the Openair package, a specialized toolkit for atmospheric data visualization and statistics, supplemented by principal component analysis, a dimension-reduction technique that reveals hidden structure in large multivariate datasets.

The headline finding is stark. In February, the city’s Air Quality Index climbed to 492, deep within the Severe category, a level at which even healthy people face serious health risks and outdoor activity becomes hazardous. By contrast, during the monsoon months the AQI fell below 100, crossing into the satisfactory range. That dramatic seasonal swing is not primarily a story about emissions rising and falling, the researchers argue, but about the atmosphere’s capacity to dilute and disperse whatever is emitted. In winter, cooler air, shallow boundary layers, and stagnant winds trap pollutants close to the ground; in the monsoon, rain scrubs the air and vigorous mixing lifts contaminants away.

To quantify that dispersal capacity, the team calculated the ventilation coefficient, a product of wind speed and the height of the atmospheric mixing layer that expresses how much clean air is available per unit time to dilute urban emissions. In Nagpur, this coefficient ranged from a low of 668 square meters per second in January to a high of 1385 square meters per second in May. The implications for policy are striking: because winter air can carry away only about half as much pollution as summer air, emission reductions in winter need to be roughly 3.7 times more aggressive than summer measures to achieve the same ambient concentration targets. In other words, a uniform, year-round abatement strategy will systematically underperform precisely when public health is most at risk.

Correlation analysis of the pollutant suite revealed which contaminants travel together, offering clues about their origins. PM10 and PM2.5 were almost inseparable, with a correlation coefficient of 0.955, indicating that the two size fractions largely share the same sources and atmospheric behavior. Carbon monoxide and benzene, meanwhile, showed a correlation of 0.889. Both are classic signatures of incomplete combustion, from vehicle exhaust, biomass burning, and other fuel-consuming processes, so their tight coupling points to common combustion sources driving a substantial share of the city’s pollution burden. This kind of statistical fingerprinting matters because it tells regulators where interventions will pay the greatest dividends: targeting combustion sources yields simultaneous reductions in several harmful pollutants at once.

Principal component analysis with varimax rotation condensed the twelve variables into two dominant components that together explained 73.1 percent of the variance in the dataset. The first component grouped the particulate pollutants with nitrogen dioxide, a cluster the authors interpret as anthropogenic combustion, the combined exhaust of traffic, industry, and burning fuels. The second component paired ozone with temperature, capturing the photochemical processes by which sunlight and heat drive the formation of secondary pollutants in the atmosphere. The separation is chemically meaningful: primary pollutants emitted directly from tailpipes and stacks behave differently from ozone, which is not emitted at all but manufactured in the air through sunlight-driven reactions involving nitrogen oxides and volatile organic compounds.

One of the study’s more surprising results is what it did not find. Many cities show a weekend effect, with pollutant concentrations dipping on Saturdays and Sundays as commuting traffic declines. In Nagpur, no statistically significant weekend effect emerged for any pollutant, with all p-values exceeding 0.1. The authors interpret this as evidence that pollution sources operate continuously, without a strong weekly rhythm dominated by commuter traffic. That pattern suggests contributions from sources that do not rest on weekends, such as industrial operations, residential fuel burning, and goods transport, and it cautions against policies that assume traffic-focused weekend restrictions will meaningfully clean the air.

The health stakes are made concrete by the study’s exceedance analysis. During winter months, PM2.5 and PM10 concentrations exceeded the standards set by India’s Central Pollution Control Board on 27 to 29 days, meaning that for most of the season the city’s most dangerous pollutants were out of compliance nearly every day. Fine particulate matter is especially concerning because particles smaller than 2.5 micrometers penetrate deep into the lungs and enter the bloodstream, contributing to cardiovascular and respiratory disease. Benzene, also tracked in the study, is a known carcinogen, adding a toxic dimension to the combustion-driven pollution mix identified by the correlation and component analyses.

From these findings the researchers distill a set of policy recommendations aimed specifically at cities like Nagpur rather than at Delhi or Mumbai. Emission reduction targets should vary by season, with the heaviest cuts demanded in winter when the ventilation coefficient collapses. Co-benefit strategies that target combustion sources should be prioritized, since a single intervention there reduces particulates, carbon monoxide, benzene, and nitrogen dioxide simultaneously. And monitoring and enforcement resources should be reallocated toward the winter months, when dispersion is poorest and every ton of emissions does the most damage. The study’s methodological approach, combining Openair’s visualization and statistical capabilities with principal component analysis on continuous monitoring data, offers a replicable template that other data-rich but understudied cities can adopt.

Beyond its immediate findings, the work highlights a growing gap in global air quality science. Most research attention and most pollution control infrastructure have concentrated on megacities, yet hundreds of Tier-2 cities across Asia and Africa are industrializing and motorizing rapidly, often with weaker monitoring networks and less tailored policy. Nagpur’s baseline shows that the dynamics in such cities, from the magnitude of seasonal swings to the absence of a weekend effect, can differ in ways that matter for regulation. As continuous monitoring stations proliferate and open-source analytical tools lower the barrier to sophisticated analysis, the authors’ demonstration that a single year of CAAQMS data can yield actionable, seasonally resolved insight may prove as influential as the specific numbers they report for Nagpur itself.

Subject of Research: Statistical analysis of air quality dynamics and meteorological influences in Nagpur using the Openair R package and principal component analysis

Article Title: Application of Openair package in R programming and PCA: unveiling air quality dynamics and meteorological influences

Article References: Chavan, A., & Lataye, D. H. (2026). Application of Openair package in R programming and PCA: unveiling air quality dynamics and meteorological influences. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-026-38275-w

Image Credits: AI Generated

DOI: 10.1007/s11356-026-38275-w

Keywords: air pollution, Openair, R programming, principal component analysis, Nagpur, PM2.5, PM10, ventilation coefficient, Air Quality Index, meteorology, combustion sources, Tier-2 cities