The study, led by Rafi Bin Shahadat of Sonargaon University with colleagues from Bangladesh University of Engineering and Technology and the New Jersey Institute of Technology, examined records of fine particulate matter known as PM2.5 and coarser particles called PM10 collected at sixteen continuous monitoring stations operated by Bangladesh’s Department of Environment between 2018 and 2025. These two pollutants are the primary targets of air quality regulation worldwide because of their ability to penetrate deep into the lungs and, in the case of the finer fraction, to cross into the bloodstream. The researchers evaluated annual, monthly, and seasonal variability using descriptive statistics, non-parametric comparisons, and Spearman correlation, and applied the Seasonal Mann-Kendall test to detect underlying trends in the data.

The headline numbers are stark. In every single year of the study period, every one of the sixteen stations exceeded both Bangladesh’s own National Ambient Air Quality Standards and the far stricter guidelines issued by the World Health Organization. The network-wide annual mean concentration of PM2.5 stood at 91.54 micrograms per cubic meter in 2018, more than eighteen times the WHO annual guideline value. PM10 averaged 155.7 micrograms per cubic meter that same year. For context, the WHO recommends that annual PM2.5 exposure stay at or below 5 micrograms per cubic meter, a threshold that even the cleanest monitoring location in Bangladesh never approached.

Yet the analysis also uncovered a genuine, if fragile, sign of progress. By 2025, the network-wide annual mean PM2.5 had fallen to 77.64 micrograms per cubic meter, a decline of 15.2 percent, while PM10 dropped from 155.7 to 132.3 micrograms per cubic meter, a 15.0 percent reduction. However, the researchers emphasize that these decreases were non-monotonic. Concentrations reached a minimum around 2020, coinciding with the COVID-19 pandemic and its associated lockdowns, then rebounded as economic activity resumed. This pattern suggests that the apparent improvement owes part of its magnitude to a temporary disruption rather than a sustained structural transformation of the country’s emission profile, and that the post-pandemic rebound has partly eroded earlier gains.

The most dramatic feature of the data is seasonal. Mean winter concentrations of PM2.5 were approximately 4.2 times higher than those recorded during the monsoon, and winter PM10 ran about 3.0 times above monsoon levels. The physical explanation lies in the interaction between emission patterns and South Asian meteorology. During the dry winter months, cooler temperatures and persistent temperature inversions trap pollutants near the surface, while reduced rainfall removes far fewer particles through wet deposition. Brick kilns, which burn coal and wood in large numbers across the Dhaka region and beyond, operate at peak intensity in the dry season, and transboundary transport of pollution from upwind regions adds to the domestic load. The monsoon, by contrast, scours the atmosphere with heavy rain and strong winds, temporarily flushing the air clean.

That meteorological control emerges clearly from the correlation analysis. Particulate concentrations were generally negatively associated with temperature, relative humidity, wind speed, and rainfall, meaning that pollution fell when these cleansing and dispersing factors strengthened. This relationship cuts both ways for policy. It implies that some portion of the year-to-year variability in measured concentrations reflects the vagaries of weather rather than changes in emissions. It also implies that emission reductions are most critical during the winter window, when the atmosphere has the least capacity to dilute what is released. The researchers found that most seasonal trends were statistically non-significant, indicating that interannual variability, rather than a consistent nationwide downward trend, characterized the study period.

The health risk calculations translate these concentrations into human terms. Using established risk assessment frameworks, the team computed the Excess Lifetime Cancer Risk for PM2.5 exposure and Hazard Quotients for both pollutants. The mean ELCR values ranged from 0.476 to 0.950, figures that sit well above the range generally considered acceptable by regulatory agencies, which typically treat risks up to one in a million, expressed as 0.000001, as tolerable. Mean Hazard Quotients came out at 3.60 for PM2.5 and 2.73 for PM10, both exceeding the threshold value of 1.0 that signals potential non-carcinogenic health effects. A Hazard Quotient above 1 indicates that the estimated exposure dose surpasses a reference level considered safe, implying meaningful risk of respiratory and cardiovascular harm across the exposed population.

These risk estimates align with a growing body of epidemiological evidence linking Bangladesh’s particulate burden to serious disease outcomes. Earlier studies in Dhaka have associated combustion-derived fine particle exposure with cardiovascular morbidity and mortality, and the International Agency for Research on Cancer classifies outdoor air pollution as a human carcinogen. Fine particles are particularly insidious because their small size allows them to lodge in the deepest recesses of the lungs, where they trigger inflammation, oxidative stress, and, over years of chronic exposure, contribute to cancers, heart disease, strokes, and chronic respiratory illness. In a densely populated country where hundreds of millions of people live in affected urban corridors, the population-level burden of disease attributable to particulate pollution is enormous.

The researchers acknowledge limitations in their approach, most notably in spatial mapping. They employed kriging, a geostatistical interpolation technique, to visualize pollution patterns across the country, but found its predictive performance limited by the sparse and uneven distribution of monitoring stations. They therefore restricted the kriging output to exploratory visualization rather than treating it as a predictive map. This is a common challenge in South Asian air quality science: reliable ground-based monitoring remains concentrated in major cities, leaving large rural and peri-urban areas effectively unmeasured. The authors call for expanded air quality monitoring infrastructure to close these gaps and to enable more robust spatial modeling in the future.

The policy implications of the study are direct. The authors argue that their findings support stronger particulate emission controls, expanded monitoring, and seasonally targeted interventions aimed at reducing population exposure. In practical terms, that means cracking down hardest on winter sources such as brick kilns, construction dust, and unpaved road traffic, while preparing public health advisories timed to the dry season when concentrations peak. The researchers suggest that the modest downward drift in annual means, however fragile, shows that interventions can move the needle, but the rebound after 2020 demonstrates that progress is reversible. For a country whose air has repeatedly ranked among the most toxic on Earth, the message of this analysis is that improvement is possible, but that without sustained structural emission reductions, Bangladesh’s residents will continue to carry a cancer risk many orders of magnitude above what international guidelines deem safe.

Subject of Research: Spatiotemporal variability, trends, and health risks of ambient PM2.5 and PM10 pollution in Bangladesh during 2018–2025

Article Title: Spatiotemporal variability, trends, and health risk assessment of ambient PM2.5 and PM10 in Bangladesh during 2018–2025

Article References: Shahadat, R. B., Keya, S. S., & Ali, M. T. (2026). Spatiotemporal variability, trends, and health risk assessment of ambient PM2.5 and PM10 in Bangladesh during 2018–2025. Environmental Geochemistry and Health, 48(15), Article 609. https://doi.org/10.1007/s10653-026-03506-z

Image Credits: AI Generated

DOI: 10.1007/s10653-026-03506-z

Keywords: PM2.5, PM10, air pollution, Bangladesh, health risk assessment, winter smog, Excess Lifetime Cancer Risk, Hazard Quotient, meteorology, Seasonal Mann-Kendall test, WHO air quality guidelines, Department of Environment

The research, led by Murat Şan of Gümüşhane University together with Sinan Nacar of Tokat Gaziosmanpaşa University and Murat Kankal of Bursa Uludağ University, examined long-term monthly total precipitation records spanning 1969 to 2020. Rather than lumping the year into the familiar four seasons, the team treated each calendar month as its own season, a choice that matters enormously in a country where a single region can receive the bulk of its annual water in winter while another depends on spring and autumn storms. This monthly resolution allowed the researchers to detect shifts that coarser seasonal averages would smooth into invisibility.

At the heart of the study are two statistical engines that approach the same question from very different angles. The first is the seasonal Mann–Kendall test, a workhorse of hydrology that checks whether a time series is drifting upward or downward while explicitly accounting for the natural rhythm of the seasons. Because it is non-parametric, it makes no assumption that the data follow a tidy bell curve, which is essential for precipitation, a variable notorious for wild swings, occasional zeros and stubborn skew. The second tool is newer and more visual: the innovative polygon trend analysis framework, paired here with a Wilcoxon significance test, which plots data against themselves in a polygon and lets researchers see not just whether a trend exists but how low, medium and high values within each month behave differently.

The contrast between the two methods is itself one of the study’s most instructive findings. Across the country, the seasonal Mann–Kendall test flagged significant increases at roughly 7 percent of station-month combinations and significant decreases at about 6 percent, a near balance that captures the genuine tug-of-war between wetting and drying influences. The innovative polygon approach, however, proved more sensitive, identifying significant or weak trend signals at a larger number of stations, particularly when those signals were tied to altitude. For water managers, this matters: a method that stays silent about subtle changes may lull decision-makers into planning for a climate that no longer exists.

Geographically, the study draws a clean line between the margins and the core of the country. Along the Aegean and Black Sea coasts, the rate of precipitation increase outpaces the rate of decrease, suggesting that these maritime corridors are, on balance, getting wetter. The Black Sea coast, fed by moisture-laden air masses riding in from the north, and the Aegean, influenced by Mediterranean storm tracks, both show this coastal intensification. In the inland regions and the east, the picture reverses: decreases dominate, a pattern that will resonate uneasily in a part of the world where snowpack on the eastern mountains feeds the headwaters of the Tigris and Euphrates.

The innovative polygon method added nuance that the classical test missed, pointing to more pronounced increasing trends in the Marmara and Mediterranean regions than the seasonal Mann–Kendall analysis alone revealed. The Marmara region, home to Istanbul and a dense concentration of industry and population, and the Mediterranean coast, a hub of agriculture and tourism, would both face very different management challenges if their rainfall regimes are indeed strengthening. The discrepancy between the two methods is not a failure of either; it is a reminder that trend detection is partly a matter of perspective, and that graphical approaches can surface structure that rank-based tests compress into a single statistic.

Perhaps the most striking thread running through the results is the role of elevation. The researchers stratified their analysis by altitude and slope, alongside geographical region and the Köppen climate classification, and found that while analogous trends appeared at lower slopes and altitudes, the trends were more strongly related to altitude than to any other factor. The rate of precipitation increase, they report, diminishes as altitude rises. In other words, the lowlands are absorbing the wetting signal while the high country is not keeping pace, a pattern that echoes a growing international literature on elevation-dependent climate change in mountain environments.

The implications of that elevation gradient ripple far beyond academic curiosity. Mountain precipitation in eastern Anatolia accumulates as snow through winter and melts through spring, timing the release of water to downstream reservoirs, irrigated fields and hydropower plants across Türkiye and its neighbors. If increases in precipitation are concentrated at low elevations while higher terrain sees weaker gains or outright declines, the seasonal storage function of the mountains may erode even as some lowland gauges record wetter years. Farmers who depend on irrigation schedules calibrated to historical runoff, and dam operators who time releases around snowmelt, would both feel the consequences of such a shift.

The study’s framing of each month as a separate season also carries practical weight for agriculture. Conventional trend analyses that overlook periodicity can blur the distinction between, say, a drying October and a wetting February, yet these two signals demand opposite responses from anyone deciding when to plant, when to irrigate and when to conserve. By resolving trends month by month and testing them with complementary statistical machinery, the researchers have produced a picture granular enough to inform water resource planning and irrigated agriculture at the scale where decisions are actually made, from the cotton fields of the Aegean lowlands to the orchards of the Mediterranean coast.

Türkiye sits within the Mediterranean climate change hotspot, one of the regions projected to experience some of the sharpest drying and most volatile hydrology as the century progresses, and the new findings add observed, station-based evidence to that global picture. The near-even split between stations showing increases and decreases nationwide, the coastal-versus-inland divide, and the altitude dependence of the trends together sketch a country whose water future will not be decided by a single national number but by a mosaic of local trajectories. For a nation whose rivers cross borders and whose agriculture anchors rural economies, understanding that mosaic, the authors suggest, is no longer optional; it is the foundation on which the next half-century of water policy will have to be built.

Subject of Research: Seasonal trend analysis of monthly precipitation across Türkiye's topographically complex and climatically diverse regions from 1969 to 2020.

Article Title: Monthly precipitation trends in Türkiye under complex topography and varying climate: a seasonal Mann–Kendall and innovative polygon trend framework

Article References: Şan, M., Nacar, S., & Kankal, M. (2026). Monthly precipitation trends in Türkiye under complex topography and varying climate: a seasonal Mann–Kendall and innovative polygon trend framework. Theoretical and Applied Climatology, 157(10), Article 619. https://doi.org/10.1007/s00704-026-06538-8

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06538-8

Keywords: precipitation trends, Türkiye, climate change, seasonal Mann-Kendall test, innovative polygon trend analysis, Köppen climate classification, altitude effects, water resources, irrigated agriculture, Mediterranean climate, Black Sea coast, hydrology