Variability Of Particulate Matter Research Articles

Seasonal anomaly of particulate matter concentration in an equatorial climate: Evaluating the transboundary impact from neighboring provinces on Padang City, Indonesia.

This study investigated the anomalous seasonal variations in particulate matter (PM) concentrations-specifically PM2.5 and PM10-in Padang City, Indonesia, situated within the Equatorial climate zone. A one-year dataset of half-hourly PM measurements from January to December 2023, collected by the Air Quality Monitoring System (AQMS) managed by the Environmental Agency of West Sumatra (DLH), was utilized. Maps of hotspots and air mass backward trajectories were used to identify possible transboundary emissions affecting Padang City. Despite the region experiencing nearly continuous rainfall, significant elevations in PM levels were observed during the typically drier months of August to October. Specifically, PM2.5 levels peaked at 36.57µg/m3 and PM10 at 39.58µg/m3 in October, significantly higher than in other months and indicating a substantial deviation from the typical expectations for equatorial climates. These results suggest that the high PM concentrations are not solely due to local urban emissions or normal seasonal variations but are also significantly influenced by transboundary smoke from peatland fires and agricultural burning in neighboring provinces such as Bengkulu, Riau, Jambi, and South Sumatra. Backward trajectory analysis further confirmed the substantial impact of regional activities on degradation of air quality in Padang City. The study underscores the need for integrated air quality management that includes both local and transboundary pollution sources. Enhanced monitoring, public engagement, and inter-regional collaboration are emphasized as crucial strategies for mitigating the adverse effects of PM pollution in equatorial regions like Padang City.

Seasonal and Spatial Variations in Particulate Matter, Black Carbon and Metals in Delhi, India’s Megacity

This study explores the spatial patterns of particulate matter (PM) in the megacity of Delhi. A GRIMM aerosol spectrometer is used to analyze different aerodynamic diameters (PM10, PM2.5, PM1.0), inhalable, thoracic, and alveolic particles, and black carbon (BC) at six prominent locations in Delhi during summer and winter. Additionally, metals (Pb, Fe, Ca, Al, Zn), along with silicon and sulfur, are analyzed using an ED-XRF spectrometer over the sampling locations during the summer season. The sampling site data are interpolated using the Kriging method to generate spatial maps to explore the air pollution problem in Delhi. East Delhi is observed to be the most polluted site, while Guru Gobind Singh Indraprastha University (GGSIPU) is the least polluted site. We further observe a high correlation between Al-Fe, Al-Ca, Zn-Pb, Ca-Fe, and S-Zn, indicating their common source of emission. Aerosols are also found to be highly enriched with metals like Al, S, Fe, Zn, and Pb, suggesting strong anthropogenic sources of these metals. Construction activities, resuspended dust, an increased number of vehicles, faulty agricultural practices, and soil could be recognized as major sources of the particulate concentration in an urban area like Delhi.

Association between exposure to ambient air pollution, meteorological factors and atopic dermatitis consultations in Singapore—a stratified nationwide time-series analysis

Atopic dermatitis (AD) is a chronic inflammatory skin disease affecting approximately 20% of children globally. While studies have been conducted elsewhere, air pollution and weather variability is not well studied in the tropics. This time-series study examines the association between air pollution and meteorological factors with the incidence of outpatient visits for AD obtained from the National Skin Centre (NSC) in Singapore. The total number of 1,440,844 consultation visits from the NSC from 2009 to 2019 was analysed. Using the distributed lag non-linear model and assuming a negative binomial distribution, the short-term temporal association between outpatient visits for AD and air quality and meteorological variability on a weekly time-scale were examined, while adjusting for long-term trends, seasonality and autocorrelation. The analysis was also stratified by gender and age to assess potential effect modification. The risk of AD consultation visits was 14% lower (RR10th percentile: 0.86, 95% CI 0.78–0.96) at the 10th percentile (11.9 µg/m3) of PM2.5 and 10% higher (RR90th percentile: 1.10, 95% CI 1.01–1.19) at the 90th percentile (24.4 µg/m3) compared to the median value (16.1 µg/m3). Similar results were observed for PM10 with lower risk at the 10th percentile and higher risk at the 90th percentile (RR10th percentile: 0.86, 95% CI 0.78–0.95, RR90th percentile: 1.10, 95% CI 1.01–1.19). For rainfall for values above the median, the risk of consultation visits was higher up to 7.4 mm in the PM2.5 model (RR74th percentile: 1.07, 95% CI 1.00–1.14) and up to 9 mm in the PM10 model (RR80th percentile: 1.12, 95% CI 1.00–1.25). This study found a close association between outpatient visits for AD with ambient particulate matter concentrations and rainfall. Seasonal variations in particulate matter and rainfall may be used to alert healthcare providers on the anticipated rise in AD cases and to time preventive measures to reduce the associated health burden.

Trend Analysis of Particulate Matters Over Kathmandu Valley, 2020

Air pollution has been a major problem of 21st century both for developed and developing countries. It has negative impact on various environmental aspects which directly or indirectly affect the quality of human health. This study analyzed daily and monthly variation of Particulate matter; PM2.5 and PM10 in air pollution at four different locations Sankhapark, Bhaktapur, Ratnapark, and Bhaisipati of Kathmandu Valley in year 2020. The data calculated has been compared to the data of United State Environmental Protection Agency (US EPA) standard. The results shows that the PM10 and PM2.5 varies in between Good to Unhealthy zone. Because of lockdown due to COVID-19 (From 24 March), pollution in Kathmandu decreased to minimum values being in Good zone and started to move towards moderate and USG –Category after lockdown is over. The Air quality Index lies in Good range from April to August. Values of AQI (PM2.5) and AQI(PM10) are well correlated and nature of variation of PM2.5 and PM10 are nearly same but magnitudes are changing in corresponding month of year 2020.

Efficacy of Low-Cost Sensor Networks at Detecting Fine-Scale Variations in Particulate Matter in Urban Environments.

The negative health impacts of air pollution are well documented. Not as well-documented, however, is how particulate matter varies at the hyper-local scale, and the role that proximal sources play in influencing neighborhood-scale patterns. We examined PM2.5 variations in one airshed within Indianapolis (Indianapolis, IN, USA) by utilizing data from 25 active PurpleAir (PA) sensors involving citizen scientists who hosted all but one unit (the control), as well as one EPA monitor. PA sensors report live measurements of PM2.5 on a crowd sourced map. After calibrating the data utilizing relative humidity and testing it against a mobile air-quality unit and an EPA monitor, we analyzed PM2.5 with meteorological data, tree canopy coverage, land use, and various census variables. Greater proximal tree canopy coverage was related to lower PM2.5 concentrations, which translates to greater health benefits. A 1% increase in tree canopy at the census tract level, a boundary delineated by the US Census Bureau, results in a ~0.12 µg/m3 decrease in PM2.5, and a 1% increase in "heavy industry" results in a 0.07 µg/m3 increase in PM2.5 concentrations. Although the overall results from these 25 sites are within the annual ranges established by the EPA, they reveal substantial variations that reinforce the value of hyper-local sensing technologies as a powerful surveillance tool.

2019 Southeast Asia Transboundary Haze and its Influence on Particulate Matter Variations: A Case Study in Kota Kinabalu, Sabah

<abstract><p>In 2019, Malaysia faced a deterioration of air quality due to transboundary haze, which brought negative implications, especially for public health. In light of the above scenario, continuous particulate matter (PM<sub>10</sub>, PM<sub>2.5</sub> and PM<sub>1</sub>) and meteorological parameters amid the haze period were taken to unravel the influence of haze on particulate matter variations and to investigate the association between particulate matter concentrations with meteorological parameters and fire hotspots in Kota Kinabalu, where it is rarely studied. Particulate matter and the meteorological parameters were monitored during the haze season, continuously from 21 August–30 September 2019, using AirMate, a ground-based air monitoring equipment. Air mass backward trajectories were simulated using the HYSPLIT Model, and fire hotspot data was obtained from the Greenpeace Global Fire Dashboard. The results showed increasing particulate matter concentrations during the haze period, with PM<sub>2.5</sub> exceeding the New Ambient Air Quality Standards (2020) on multiple days. For meteorological parameters, all parameters showed a significant weak positive relationship with respective particulate matter. However, the correlation between particulate matter and fire hotspots in Indonesia showed a moderate positive relationship. The backward trajectories simulated indicated the influence of south-westerly winds in transporting the pollutants from fire hotspots in the Indonesia region. Thus, we provide beneficial information about the impacted area during the 2019 transboundary haze episode, where the interactions between the particulate matter variations and the parameters studied were unraveled.</p></abstract>

Winter and spring variation in sources, chemical components and toxicological responses of urban air particulate matter samples in Guangzhou, China

The sources and chemical components of urban air particles exhibit seasonal variations that may affect their hazardousness to human health. Our aims were to investigate winter and spring variation in particulate matter (PM) sources, components and toxicological responses of different PM size fractions from samples collected in Guangzhou, China. Four size-segregated PM samples (PM10–2.5, PM2.5–1, PM1–0.2, and PM0.2) were collected separately during winter (December 2017 and January 2018) and spring (March 2018). All PM samples were analyzed for chemical components and characterized by source. RAW 264.7 macrophages were exposed to four doses of PM samples for 24 h. Cytotoxicity, oxidation, cell cycle, genotoxicity and inflammatory parameters were tested. PM concentrations were higher in the winter samples and caused more severe cytotoxicity and oxidative damage than to PM in the spring samples. PM in winter and spring led to increases in cell cycle and genotoxicity. The trends of size-segregated PM components were consistent in winter and spring samples. Metallic elements and PAHs were found in the largest concentrations in winter PM, but ions were found in the largest concentrations in spring PM. metallic elements, PAHs and ions in size-segregated PM samples were associated with most toxicological endpoints. Soil dust and biomass burning were the main sources of PM in winter, whereas traffic exhaust and biomass burning was the main source with of spring PM. Our results suggest that the composition of PM samples from Guangzhou differed during winter and spring, which led to strong variations in toxicological responses. The results demonstrate the importance of examining a different particle sizes, compositions and sources across different seasons, for human risk assessment.

Well Played: Using Game App Data to Assess Wildfire Smoke and Cognitive Performance

Well Played: Using Game App Data to Assess Wildfire Smoke and Cognitive Performance

Spatial and Temporal Variation of Particulate Matter Around Lagos Environs

Air monitoring is very essential for characterizing health risks, formulating cost-effective abatement strategies, creating and enforcing appropriate air quality standards, and as well as healthy urban planning. This study investigates the spatial and temporal variation of aerosol pollution in Lagos environs, Nigeria. Selected sampling locations were Computer Village (CV), Oshodi Garage (OG), Magodo Estate (MG), and Caleb International College (CIC). Associated anthropogenic activities were the basis for the selection of the sampling location. Measurements of particulate matters were undertaken by employing the AEROCET 531S device in these selected residential, academic, and commercial environments across Lagos State. The measurements were taken at approximately 2 m from the ground in each sampling location. Data analysis and visualization were executed with python to enhance the visualization of the spatial and temporal distribution of the different particulate matter within and across the locations. A pronounced gradient of the particulate matter concentrations (µg/m3) was observed across the study area. The study outcome demonstrates that particulate matter distribution is dominated by PM10 in all locations under consideration. The proportions of fine particles to coarse particles in these environments were low ranging from 0.027 – 0.277 in CV, 0.018 – 0.395 in OG, 0.080 – 0.291 in MG, and 0.028 – 0.272 in CIC. Findings from this study established the dominance of coarse particles assumed to be contributed by cement dust, coal dust, flour, paint spray, pollen, cloud droplets, etc.

Ozone and Particulate Matter Variations in Southwest Ohio, USA, during the COVID-19 Pandemic in 2020

In this study, ozone and particulate matter variations from four monitoring stations in the Southwest Ohio region were analyzed at different stages of the COVID-19 pandemic in 2020, and compared with those in 2019. These stations include a US EPA NCore site (Taft), an urban-suburban site (Sycamore), an industrial source site (Yankee) and a residential site near the source (Amanda). The air quality data were broken down to the lockdown period (March 23 to May 31) and the re-opening periods from June to December, 2020. Publicly available monitoring data on PM2.5, ozone and PM10 were used for analysis. PM2.5 reductions were non-uniform with strong seasonal variations. PM2.5 reductions were 4.04%, 15.6%, 11.63% at Sycamore, Taft and Yankee sites respectively during the lockdown, but increased 11.23% at Amanda. Reductions at Taft may be related to traffic restrictions while those at Yankee may be due to both reduced industrial production and source control measures. Ozone reductions were 7.94% and 6.50% at Sycamore and Taft sites during the lockdown with Sycamore having higher ozone concentrations pre, during and post lockdown. Ozone formation is NOx-limited in Southwest Ohio region and the variations are uniform. Lower temperatures during the lockdown and fall of 2020 can also be a contributing factor. The Air Quality Index (AQI) of combined ozone and PM2.5 improved during the pandemic year. Consistent with a few other studies, COVID-19 restrictions did not bring uniform pollutant reductions to the Southwest Ohio region.

Ozone and Particulate Matter Variations in Southwest Ohio, USA, during the COVID-19 Pandemic in 2020

In this study, ozone and particulate matter variations from four monitoring stations in the Southwest Ohio region were analyzed at different stages of the COVID-19 pandemic in 2020, and compared with those in 2019. These stations include a US EPA NCore site (Taft), an urban-suburban site (Sycamore), an industrial source site (Yankee) and a residential site near the source (Amanda). The air quality data were broken down to the lockdown period (March 23 to May 31) and the re-opening periods from June to December, 2020. Publicly available monitoring data on PM2.5, ozone and PM10 were used for analysis. PM2.5 reductions were non-uniform with strong seasonal variations. PM2.5 reductions were 4.04%, 15.6%, 11.63% at Sycamore, Taft and Yankee sites respectively during the lockdown, but increased 11.23% at Amanda. Reductions at Taft may be related to traffic restrictions while those at Yankee may be due to both reduced industrial production and source control measures. Ozone reductions were 7.94% and 6.50% at Sycamore and Taft sites during the lockdown with Sycamore having higher ozone concentrations pre, during and post lockdown. Ozone formation is NOx-limited in Southwest Ohio region and the variations are uniform. Lower temperatures during the lockdown and fall of 2020 can also be a contributing factor. The Air Quality Index (AQI) of combined ozone and PM2.5 improved during the pandemic year. Consistent with a few other studies, COVID-19 restrictions did not bring uniform pollutant reductions to the Southwest Ohio region.

Comparative Assessment of Seasonal Variation in Size-Segregated Particulate Matters around Urban Drains

The rapidly growing urbanization has resulted in increased environmental pollution and degradation that has equally affected the quality of air and wastewater. Urban drains that carry wastewater from households and industries are also carrying loads of pollutants. The current study was conducted to compare the trend in the concentration of particulate matter (PM) in the air, across the seasons near the urban drains in Delhi, a metropolitan city. The air samples were taken from Najafgarh drain, which is the biggest drain of Delhi city and has a contribution of about 60% of the total wastewater released from Delhi city into river Yamuna. The Indian Standard method IS 5182 Part 24:2019 was used for PM2.5 & IS 5182 Part 23:2006 was used for PM10 monitoring and analysis. The results of the analysis showed that the variation in particulate matter (PM) is observed with changes in relative humidity and with seasonal changes. The variation in PM2.5 levels was seen in a range of 55.30-118.42 μg/m3 from June 2020 to February 2021 & in PM10 levels were seen in the range of 77.54-261.99 μg/m3 during June 2020 to February 2021 for location L1. The variation in PM2.5 levels was seen in a range of 23.84-76.94 μg/m3 from June 2020 to February 2021 & in PM10 levels were seen in the range of 42.06-149.48 μg/m3 during June 2020 to February 2021 for location L2. Few studies have shown that contaminants can enter into the fresh air from the municipal wastewaters and open drains.

Innovative use of essential oil cold diffusion system for improving air quality on indoor cultural heritage spaces

Bioaerosols play an important role in Cultural Heritage (CH) spaces air quality and biodeterioration risk and there is a growing interest to reduce them in actual conservation strategies. Essential oils (EOs) have been well-known in several fields for their antimicrobial properties and they have found use in a number of applications. In this work Melaleuca alternifolia (Tea tree) and Thymus vulgaris (Thyme) EOs are cold diffused in unventilated lab spaces for reducing air bacterial and fungi contamination. Moreover, Tea tree EO was also tested in an unventilated real CH space. The effectiveness of EOs in reducing air bacterial and fungi contamination in unventilated indoor spaces during 24 h was evaluated. Air quality was also analyzed by means of temperature, humidity and particulate matter variation. EOs’ diffusion system CH artworks risks was studied by means of thermography, diffusion range and EOs compounds deposition on the artistic materials by GC–MS. Tea tree EOs vaporization showed the best results with 77,3% and 95,0% fungi and bacteria air contamination reduction, with no thermohydrometric alteration and absence of EOs components deposition on the artistic vault.

Spatial and seasonal variations in particulate matter and gaseous pollutants around integrated industrial estate (IIE), SIDCUL, Haridwar: a case study

The holy city of Haridwar is now emerging as a key industrial destination of the north India due to the presence of IIE Haridwar. Due to increased anthropogenic activities like vehicular emissions and industrial discharges in the Haridwar region, air pollution levels has raised greatly. The objectives of the study were (1) to determine the Spatial and seasonal variations of air quality in and around IIE, Haridwar and (2) to find out the Air Pollution Index (API). For this monthly monitoring of pollutant gases (SO2 and NO2) and particulate matter (SPM and RSPM) for two years (2012 and 2013) were done at six ambient air quality monitoring locations, one at each land use area (rural/agricultural, urban residential, protected area, industrial and commercial land use). The average SPM, RSPM, SO2, NO2 in the study area were found as 86.92 µg/m3, 81.04 µg/m3, 23.23 µg/m3, 24.73 µg/m3, respectively. It was observed that the average SPM levels exceed WHO standard limit of 120 µg/m3 at IIE Haridwar during study period. The average API during study period was 50.14 which indicates light air pollution. Meteorological conditions along with the distance from sources, particularly road transportation play a major role in deciding the ambient air quality over a region.

Comparison of Spatial Modelling Approaches on PM10 and NO2 Concentration Variations: A Case Study in Surabaya City, Indonesia.

Because of fast-paced industrialization, urbanization, and population growth in Indonesia, there are serious health issues in the country resulting from air pollution. This study uses geospatial modelling technologies, namely land-use regression (LUR), geographically weighted regression (GWR), and geographic and temporal weighted regression (GTWR) models, to assess variations in particulate matter (PM10) and nitrogen dioxide (NO2) concentrations in Surabaya City, Indonesia. This is the first study to implement spatiotemporal variability of air pollution concentrations in Surabaya City, Indonesia. To develop the prediction models, air pollution data collected from seven monitoring stations from 2010 to 2018 were used as dependent variables, while land-use/land cover allocations within a 250 m to 5000 m circular buffer range surrounding the monitoring stations were collected as independent variables. A supervised stepwise variable selection procedure was applied to identify the important predictor variables for developing the LUR, GWR, and GTWR models. The developed models of LUR, GWR, and GTWR accounted for 49%, 50%, and 51% of PM10 variations and 46%, 47%, and 48% of NO2 variations, respectively. The GTWR model performed better (R2 = 0.51 for PM10 and 0.48 for NO2) than the other two models (R2 = 0.49–0.50 for PM10 and 0.46–0.47 for NO2), LUR and GWR. In the PM10 model four predictor variables, public facility, industry and warehousing, paddy field, and normalized difference vegetation index (NDVI), were selected during the variable selection procedure. Meanwhile, paddy field, residential area, rainfall, and temperature played important roles in explaining NO2 variations. Because of biomass burning issues in South Asia, the paddy field, which has a positive correlation with PM10 and NO2, was selected as a predictor. By using long-term monitoring data to establish prediction models, this model may better depict PM10 and NO2 concentration variations within areas across Asia.

Effects of air quality on house prices: evidence from China’s Huai River Policy

We employ a regression discontinuity (RD) design to estimate the impact of air pollution on house prices across a river that demarcates regions with and without coal-fired heating resulting from the Huai River Policy. This policy was decreed by the Chinese government in the 1950s and mandated the burning of coal for indoor heating at subsidised prices north of the Huai River. Employing quasi-experimental variation in particulate matter of 10 micrometres or less in aerodynamic diameter (PM10) generated by this arbitrary policy and a regression discontinuity (RD) design based on distance from Huai River, we estimate the local average treatment effect (LATE) to provide new evidence on the capitalisation of PM10 air pollution into house values. By using panel data covering 30 large cities on either side of the river for the period 2006–2015, we found that 1 µg/m3 (micrograms per cubic metre) reduction in average PM10 is associated with an approximately 1% increase in house prices. The results are robust to using parametric and nonparametric estimation methods, adjustment to a rich set of covariates, and using a subsample excluding first-tier cities.

Using overlapping VIIRS scenes to observe short term variations in particulate matter in the coastal environment

In coastal areas, the concentrations and the optical properties of the water components have a large spatial and temporal variability, due to river discharges and meteo-marine conditions, such as wind, wave and current, and their interaction with shallow water bathymetry. This large temporal variability cannot be captured using the standard Ocean Colour Radiometry (OCR) polar orbiting satellites, the latter providing almost one image per day. On the contrary, the use of OCR geostationary sensors, like the Geostationary Ocean Colour Imager (GOCI), centred above the Korean Peninsula, enable to capture the short-term variability of the optical properties. To compensate the lack of a geostationary sensor similar to GOCI over other coastal environments, like the North Adriatic Sea (NAS), the multiple observations provided during the same day by the Visible Infrared Imaging Radiometer Suite (VIIRS) mounted on the SUOMI NPP satellite, can be exploited. Indeed, due to its large swath of 3060 km, the VIIRS orbits can overlap over the NAS during the same day within 1 h and 42 min, an important feature that can be useful in capturing the short term variability of the optical properties.A large number of VIIRS overlaps in the NAS are characterized by high sensor zenith angle (SZA) of the observation, resulting in a large portion of images masked by the high satellite zenith flag. In order to make available those observations and, in general, to reduce the dependence of the VIIRS observations from the SZA, an adjustment based on a multi linear regression scheme, which exploits radiometric in situ observations, was here applied.This study aims to prove the suitability of the adjusted overlapping VIIRS in capturing the short time scale dynamics of particulate backscattering, and this was demonstrated by the analysis of a case study for the 21st and 22nd of March 2013. In order to evaluate the advantages in using multiple observations during the same day, also the ~24 h dynamics was analysed, comparing the overlapping VIIRS results with the ones obtained from the daily product.

Variation of Particulate Matter (PM10) in a Semi-urban Area during 2015 Malaysia Haze

Particulate matter (PM10) is one of the most dangerous air pollutants released during haze phenomena which can disrupt lung function, circulatory system and reduce visibility. The aims of this study were to determine the trend of PM10 in Tanjung Malim, Perak during 2015 haze episode. The four months trend of PM10 was analyzed, along with daily trend for the highest concentration of PM10 recorded during the haze episode. In addition, the correlation between PM10 and meteorological parameters, such as wind speed, temperature, and humidity were also analyzed. Result indicated that, PM10 level during September and October 2015, have mostly exceeded the Recommended Malaysian Air Quality Guidelines (RMAQG) by Malaysian Government (150 μg/m3). From the 72h backward trajectories, it can be stated that most of the pollutants came from biomass burning in neighbouring country, during the southwest monsoon. Changes in PM10 concentration were possibly influenced by meteorological factors which play an important role in haze episode.

CHARACTERISING SEASONAL VARIATIONS AND SPATIAL DISTRIBUTION OF AMBIENT PM2.5 CONCENTRATION BASED ON SHORT-TERM MONITORING IN DARKHAN CITY

The purpose of this study was to survey the seasonal variation of fine particulate matter (PM2.5) concentration and determine spatial distribution in Darkhan city. Air pollution research and reports have been few and far between in most of parts in Mongolia, especially in Darkhan with respect to quantitative aerosol particle concentration. In this study, we utilized “PM2.5sensor” to measure spatial and seasonal variation of particulate matter concentrations in the study area. The monitoring points were chosen by basing on their specific features and set up directly at ambience outdoor. In each season, we carried out measurement at 3 points, which covered the ger district and apartment district areas for one day. Whereas, at one point the ger district was sampled for 4 days in summer. Fine particulate matter concentrations were the highest in the ger district area because there are many households that use coal for their daily heating and cooking, and at the bared surface. As for seasonal variation, in winter pollution reached 400 times higher than other seasons. Furthermore, at the ger district area, PM2.5 concentration was as much as 20 times greater than other points and it was observed that this too had its impact on the apartment district as well. As regards the air quality index, the level of particulate matter in the ger district area is extremely unhealthy to hazardous in winter. While, good and moderate indexes were mostly identified at monitoring points during the springtime.

Meteorological Factors Affecting Winter Particulate Air Pollution in Ulaanbaatar from 2008 to 2016

Ulaanbaatar, the capital of Mongolia, is subject to high levels of atmospheric pollution during winter, which severely threatens the health of the population. By analyzing surface meteorological data, ground-based LIDAR data, and radiosonde data collected from 2008 to 2016, we studied seasonal variations in particulate matter (PM) concentration, visibility, relative humidity, temperature inversion layer thickness, and temperature inversion intensity. PM concentrations started to exceed the 24-h average standard (50 μg/m3) in mid-October and peaked from December to January. Visibility showed a significant negative correlation with PM concentration. Relative humidity was within the range of 60–80% when there were high PM concentrations. Both temperature inversion layer thickness and intensity reached maxima in January and showed similar seasonal variations with respect to PM concentration. The monthly average temperature inversion intensity showed a strong positive correlation with the monthly average PM2.5 concentration. Furthermore, the temperature inversion layer thickness exceeded 500 m in midwinter and overlaid the weak mixed layer during daytime. Radiative cooling enhanced by the basin-like terrain led to a stable urban atmosphere, which strengthened particulate air pollution.