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When undertaking optical sparse projection reconstruction, the reconstruction of the tested field often requires the utilization of a priori knowledge to compensate for the lack of information due to the sparse projection angle. In order to reconstruct the radiation field of unknown materials or in situations where a priori knowledge cannot be obtained, this paper proposes an extremely sparse tomography multispectral temperature field reconstruction algorithm that analyzes the similarity (the similarity here compares and calculates the Euclidean distance of the spectral emissivity values at various wavelengths between different spectral curves) of radiation characteristics of materials under the same pressure and concentration but different temperature, describes the similarity between the radiation information of the tested field using the dynamic time warping (DTW) algorithm, and uses the similarity sum of the radiation information among the subregions of the temperature field as the optimization objective. This is combined with the equation-constrained optimization algorithm and multispectral thermometry to establish the statistical law between the missing information and finally realize the reconstruction of the temperature field. Simulation experiments show that, without any a priori knowledge, the method in this paper can realize reconstruction of the temperature field with an accuracy of 1.53-12.05% under two projection angles and has fewer projection angles and stronger robustness than other methods.

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BACKGROUND: Short-term increases in air pollution are associated with poor asthma and COPD outcomes. Short-term elevations in fine particulate matter (PM2.5) due to wildfire smoke are becoming more common. RESEARCH QUESTION: Are short-term increases in PM2.5 and ozone in wildfire season and in winter inversion season associated with a composite of emergency or inpatient hospitalization for asthma and COPD? STUDY DESIGN AND METHODS: Case-crossover analyses evaluated 63,976 and 18,514 patients hospitalized for primary discharge diagnoses of asthma and COPD, respectively, between January 1999 and March 2022. Patients resided on Utah's Wasatch Front where PM2.5 and ozone were measured by Environmental Protection Agency-based monitors. ORs were calculated using Poisson regression adjusted for weather variables. RESULTS: Asthma risk increased on the same day that PM2.5 increased during wildfire season (OR, 1.057 per + 10 µg/m3; 95% CI, 1.019-1.097; P = .003) and winter inversions (OR, 1.023 per +10 µg/m3; 95% CI, 1.010-1.037; P = .0004). Risk decreased after 1 week, but during wildfire season risk rebounded at a 4-week lag (OR, 1.098 per +10 µg/m3; 95% CI, 1.033-1.167). Asthma risk for adults during wildfire season was highest in the first 3 days after PM2.5 increases, but for children, the highest risk was delayed by 3 to 4 weeks. PM2.5 exposure was weakly associated with COPD hospitalization. Ozone exposure was not associated with elevated risks. INTERPRETATION: In a large urban population, short-term increases in PM2.5 during wildfire season were associated with asthma hospitalization, and the effect sizes were greater than for PM2.5 during inversion season.

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With the continuous development of thermal infrared remote sensing technology and the maturation of remote sensing inversion algorithms based on surface temperatures, identifying high-temperature anomalous areas by inverting surface temperatures has become an crucial approach to finding geothermal potential areas. The eastern region of Longyang in western Yunnan Province is renowned for geothermal resources, though the distribution area of geothermal potential remains unknown. Therefore, this study used Landsat-8 TIRS data and four surface temperature inversion algorithms, namely, mono-window algorithm, single-channel algorithm, Du split window algorithm (SWD), and Jiménez-Muñoz split window algorithm (SWJ), to explore the astern region of Longyang. The inversion results were compared with Moderate Resolution Imaging Spectroradiometer Land Surface Temperature (MODIS LST) results for analysis and cross-validation to select the optimal algorithm. A multi-view remote sensing temperature anomaly information extraction method was adopted. Moreover, the overall threshold method, the fracture structure buffer method, and the joint analysis of diurnal temporal data were combined for the reduction of the thermal anomaly area as well as for comprehensively defining the geothermal prospective area in the study area. The results demonstrated that the mono-window algorithm had the highest accuracy with a Pearson coefficient of 0.77, which is more suitable for the surface temperature inversion in Longyang area. Furthermore, three geothermal anomalies (A, B, and C) were identified in the study area, with larger thermal anomaly in A and C, but a smaller one in B. All three areas had hot spring points verified, with A and C exhibiting more significant development potential. The research results provide a reliable methodological basis for the development of geothermal resources in the region.

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A recently-developed radon-based method for combined classification of both diurnal and synoptic timescale changes in the atmospheric mixing state is applied to 1-year of observations in Ljubljana (capital of Slovenia). Five diurnal-timescale mixing classes (#1 to #5) were defined for each season along with an additional mixing class (#6) in non-summer months, representative of synoptic-timescale changes of the atmospheric mixing state associated with "persistent temperature inversion" (PTI) events. Seasonal composite radiosonde profiles and mean sea level pressure charts within each mixing class are used to demonstrate the link between prevailing synoptic conditions and the local mixing state, which drives changes in urban air quality. Diurnal cycles of selected pollutants (BC, NO2, CO, PM10, SO2 and O3) exhibited substantial seasonality as a result of changing mixing conditions, source types and strengths. For the more well-mixed conditions (classes #2 to #3), surface wind speeds were 3 times higher than during class #6 (PTI) conditions, resulting in a 3-fold reduction of primary pollutant accumulation. Daily-mean PM10 concentrations only exceeded EU and WHO guideline values in winter and autumn for two of the radon-defined mixing classes: (i) class #5 (strongly stable near-surface conditions associated with passing synoptic anti-cyclone systems), and (ii) class #6 (PTI conditions driven by regional subsidence in the presence of the "Siberian High"). Both mixing states were associated with low mean wind speeds (∼0-0.7 m s-1) and strong thermal stratification, as indicated both by pseudo-vertical temperature gradients (∆T/∆z) and radiosonde profiles. Diurnal ∆T/∆z values indicated limited opportunity for convective mixing of pollutants from the basin atmosphere under these conditions. The demonstrated consistency in atmospheric mixing conditions (vertically and spatially) across the diurnal cycle within each of the defined mixing classes suggests the radon-based classification scheme used in conjunction with 3-D urban sensor networks could be well suited to evaluate mitigation schemes for urban pollution and urban climate.

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Temperature inversion (TI) is one of the meteorological conditions that significantly affect regional air quality. Knowledge gap regarding the impacts of TI on surface PM2.5 in different topographies still existed. In the present study, the occurrence frequency, temperature lapse rate (TLR), depth, and the diurnal variations of TI, surface-based TI (SBTI), elevated TI (ElTI), and multiple layers of TIs (MultiTI) and their impacts on near-surface PM2.5 concentrations over eastern China that covers a range of topographies and climates, are systematically investigated based on global reanalysis ERA5 and the nationwide monitoring PM2.5 dataset from 2014 to 2020. TIs occurred mostly in the early morning. Different types of TIs present distinctive seasonal and spatial patterns. The majority of SBTIs and ElTIs occurred during nighttime in northern China and daytime in southern China, respectively, as the result of their formation mechanisms. SBTIs usually had larger TLR while ElTIs had deeper depth. SBTIs showed strong enhancement effects on PM2.5 concentration over the study domain while ElTIs showed more obvious impacts on northern nocturnal PM2.5. The peak time of PM2.5 was found around 18:00-22:00 LST, and TLR and depth of TIs are thought to be more relevant to PM2.5 peak concentration due to their coincident peak times. The strength of TIs is therefore more crucial in regulating PM2.5 than its occurrence frequency. Based on statistical analysis, our study provided a large picture of the generic spatiotemporal patterns of TIs and illustrated the impacts of different TIs on surface PM2.5 pollution on a diurnal basis. For a deeper understanding of the formation of PM2.5 pollution, more attention needs to be paid to the nocturnal PM2.5 not only at surface level but also at higher levels in the presence of TIs.

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Studies have associated the human respiratory syncytial virus which causes seasonal childhood acute bronchitis and bronchiolitis (CABs) with climate change and air pollution. We investigated this association using the insurance claims data of 3,965,560 children aged ≤ 12 years from Taiwan from 2006−2016. The monthly average incident CABs increased with increasing PM2.5 levels and exhibited an inverse association with temperature. The incidence was 1.6-fold greater in January than in July (13.7/100 versus 8.81/100), declined during winter breaks (February) and summer breaks (June−August). The highest incidence was 698 cases/day at <20 °C with PM2.5 > 37.0 µg/m3, with an adjusted relative risk (aRR) of 1.01 (95% confidence interval [CI] = 0.97−1.04) compared to 568 cases/day at <20 °C with PM2.5 < 15.0 µg/m3 (reference). The incidence at ≥30 °C decreased to 536 cases/day (aRR = 0.95, 95% CI = 0.85−1.06) with PM2.5 > 37.0 µg/m3 and decreased further to 392 cases/day (aRR = 0.61, 95% CI = 0.58−0.65) when PM2.5 was <15.0 µg/m3. In conclusion, CABs infections in children were associated with lowered ambient temperatures and elevated PM2.5 concentrations, and the high PM2.5 levels coincided with low temperature levels. The role of temperature should be considered in the studies of association between PM2.5 and CABs.

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Urban heat islands (UHIs) and their energy consumption are topics of widespread concern. This study used remote sensing images and building and meteorological data as parameters, with reference to Oke's local climate zone (LCZ), to divide urban areas according to the height and density of buildings and land cover types. While analyzing the heat island intensity, the neural network training method was used to obtain temperature data with good temporal as well as spatial resolution. Combining degree-days with the division of LCZs, a more accurate distribution of energy demand can be obtained by different regions. Here, the spatial distribution of buildings in Shenyang, China, and the law of land surface temperature (LST) and energy consumption of different LCZ types, which are related to building height and density, were obtained. The LST and energy consumption were found to be correlated. The highest heat island intensity, i.e., UHILCZ 4, was 8.17°C. The correlation coefficients of LST with building height and density were -0.16 and 0.24, respectively. The correlation between urban cooling energy demand and building height was -0.17, and the correlation between urban cooling energy demand and building density was 0.17. The results indicate that low- and medium-rise buildings consume more cooling energy.

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Cold-air pooling is a global phenomenon that frequently sustains low temperatures in sheltered, low-lying depressions and valleys and drives other key environmental conditions, such as soil temperature, soil moisture, vapor pressure deficit, frost frequency, and winter dynamics. Local climate patterns in areas prone to cold-air pooling are partly decoupled from regional climates and thus may be buffered from macroscale climate change. There is compelling evidence from studies across the globe that cold-air pooling impacts plant communities and species distributions, making these decoupled microclimate areas potentially important microrefugia for species under climate warming. Despite interest in the potential for cold-air pools to enable species persistence under warming, studies investigating the effects of cold-air pooling on ecosystem processes are scarce. Because local temperatures and vegetation composition are critical drivers of ecosystem processes like carbon cycling and storage, cold-air pooling may also act to preserve ecosystem functions. We review research exploring the ecological impacts of cold-air pooling with a focus on vegetation, and then present a new conceptual framework in which cold-air pooling creates feedbacks between species and ecosystem properties that generate unique hotspots for carbon accrual in some systems relative to areas more vulnerable to regional climate change impacts. Finally, we describe key steps to motivate future research investigating the potential for cold-air pools to serve as microrefugia for ecosystem functions under climate change.

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The winter fog/haze events in northeastern Pakistan and surrounding regions of India are often mixed with pollutants to form smog, and consequently damage human health and hampers daily life in the form of fatalities through road accidents, road blockages, and flight delays. The persistent anti-cyclonic conditions can further trigger the temperature inversion and prolong the smog event from days to weeks. The present study provides characteristics and lasting mechanisms of two persistent winter fog events (2016-2017) in Lahore, Pakistan, by using the fifth generation of European Center for Medium-Range Weather Forecast (ECMWF) ERA5 reanalysis data and National Oceanic and Atmospheric Administration (NOAA) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model simulated with Global Data Assimilation System (GDAS) meteorological data. The results showed the presence of strong low-level anti-cyclonic circulations with wind speed less than 1.5 m/s from November to January over Eastern Punjab for two foggy winter seasons. The deep inversion during the fog events was observed that prevented the natural ventilation of air in the upper atmosphere and ultimately the smoke and heavy pollutant accumulated in the lower atmosphere. Furthermore, high relative humidity greater than 83% near the ground indicates a high condensation rate for water vapors to form fog near the ground. The analysis of the NOAA HYSPLIT trajectory model at different vertical heights revealed that smoke from stubble crop burning in the first week of November 2017 in Punjab and Haryana mixed with fog under favorable stable conditions that lead to intense smog over Lahore. This study will help to understand and to develop a forecasting mechanism of fog events by characterizing the meteorological conditions of the study area and to minimize the adverse impacts of smog on public health.

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It is well known that the atmospheric boundary layer (ABL) plays a significant role in controlling the variability of atmospheric constituents such as aerosols and trace-gases. Hence, significant diurnal and seasonal variation in these will be observed as the ABL altitude does. However, on several occasions, high aerosol concentration in the lidar measurements is observed even above the ABL altitude. This raised a question that up to what extent ABL altitude acts as a capping layer for these pollutants? From the detailed analysis carried out using long-term (2010-2018) lidar observations and simultaneous radiosonde profiles obtained from Gadanki, India, we show that 'there exist thermal inversions (TI), which are stronger than the ABL inversions, that fully control the vertical extent'. The detailed characteristics of TI (inversion strength (IS) and inversion depth (ID)) are also obtained. The results revealed that aerosol concentrations below the TI altitude increases with IS (ID) up to 3-4 K (300-400 m) during winter whereas in pre-monsoon it increases up to 2-3 K (100-200 m). Thus, IS of up to 2-4 K is required to fully trap the aerosol concentrations and this TI coincide with the ABL inversions for 51.7% only, particularly during the winter and pre-monsoon seasons. This analysis is further extended to different geographical locations of India using the aerosol profiles obtained from CALIPSO and a network of 23 radiosonde stations. The observed results provided further evidence that the vertical distribution of aerosols is restricted to the maximum extent by the TI but not the ABL altitude. These observations lead us to propose a hypothesis that 'trapping of aerosols fully occurs up to particular IS and ID only and the ABL altitude is not the deciding factor most of the time for capping the aerosol vertical distribution'. These findings will greatly help in modeling the diffusion and transport of air pollutants in the lower troposphere.

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Particle number size distribution (PNSD) is of importance for understanding the mechanisms of particle growth, haze formation and climate impacts. However, the measurements of PNSD aloft in megacities are very limited. Here we report the first simultaneous winter measurements of size-resolved particle number concentrations along with collocated gaseous species and aerosol composition at ground level and 260 m in Beijing. Our study showed that the vertical differences of particle number concentrations between ground level and aloft varied significantly as a function of particle size throughout the study. Further analysis illustrated the impacts of boundary dynamics and meteorological conditions on the vertical differences of PNSD. In particular, the temperature and relative humidity inversions were one of the most important factors by decoupling the boundary layer into different sources and processes. Positive matrix factorization analysis identified six sources of PNSD at both ground level and city aloft. The local source emissions dominantly contributed to Aitken-mode particles, and showed the largest vertical gradients in the city. Comparatively, the regional particles were highly correlated between ground level and city aloft, and the vertical differences were relatively stable throughout the day. Our results point towards a complex vertical evolution of PNSD due to the changes in boundary layer dynamics, meteorological conditions, sources, and processes in megacities.

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Air pollutant accumulations during wintertime persistent cold air pool (PCAP) events in mountain valleys are of great concern for public health worldwide. Uncertainties associated with the simulated meteorology under stable conditions over complex terrain hinder realistic simulations of air quality using chemical transport models. We use the Community Multiscale Air Quality (CMAQ) model to simulate the gaseous and particulate species for 1 month in January 2011 during the Persistent Cold Air Pool Study (PCAPS) in the Salt Lake Valley (SLV), Utah (USA). Results indicate that the temporal variability associated with the elevated NOx and PM2.5 concentrations during PCAP events was captured by the model (r = 0.20 for NOx and r = 0.49 for PM2.5). However, concentrations were not at the correct magnitude (NMB = -35/12% for PM2.5 during PCAPs/non-PCAPs), where PM2.5 was underestimated during PCAP events and overestimated during non-PCAP periods. The underestimated PCAP strength is represented by valley heat deficit, which contributed to the underestimated PM2.5 concentrations compared with observations due to the model simulating more vertical mixing and less stable stratification than what was observed. Based on the observations, the dominant PM2.5 species were ammonium and nitrate. We provide a discussion that aims to investigate the emissions and chemistry model uncertainties using the nitrogen ratio method and the thermodynamic ammonium nitrate regime method.

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Surface radiation is crucial to atmospheric boundary layer development and air pollution formation. Several studies have revealed that surface radiation plays a vital role in developing the daytime convective boundary layer that controls the explosive growth of PM2.5 concentration; however, less attention has been paid to the effects of changing nighttime surface radiation on the near-surface temperature inversion layer and PM2.5 accumulation. In this study, we used long-term observations of meteorological and environmental data and atmospheric boundary layer measurements during a severe PM2.5 pollution event to investigate the effect of changes in nocturnal surface radiation on the increase in PM2.5 concentrations. The results showed that surface radiation cooling was enhanced (weakened) by decreased (increased) cloud cover fraction by changing longwave radiation at night; this strengthened (weakened) near-surface temperature inversion intensity and promoted (prevented) the accumulated increase in PM2.5. This observational study using 5-year data further confirmed the cloud radiative effect on the nighttime accumulation of PM2.5 with a significant negative correlation between nighttime averages of surface PM2.5 concentrations and cloud cover fractions. This reveals an important mechanism for the impact of surface radiation cooling modulated by cloud cover change on the nighttime accumulated increase in PM2.5. This finding extends our understanding of air pollutant accumulation at night with potential implications for atmospheric environment change.

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There is a downward curve between increasing inversion altitude and the number of coronavirus patients during all periods. As temperature inversion altitude increases, the pollutants are dispersed in a greater thickness of the atmosphere and the concentration of the pollutants decreases on the earth's surface. At the same time, the number of patients with Covid-19 reduces. Although investigation of the effect of severity of pollutants on the number of coronavirus patients showed poor significance level during the periods, a decreasing and increasing relationship was shown. in 1- and 9-14-day periods, the correlation coefficient was negative. As a result, the effect of the severity of pollutants and Covid-19 is not observed on 1- and9-14-day periods. Conversely, during2-8-day periods, a positive correlation coefficient was observed. Therefore, the time between infection with the virus and the onset of symptoms of this disease is between 2 and 8 days, in which the 3-day period showed the highest correlation. Considering the relationship between inversion altitude, the severity of pollutants and the number of patients during 2-5-day periods, it can be concluded that in the metropolitan city of Tehran, the maximum infection of this virus and the onset of symptoms is between 2 and 5 days.

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Air pollution is the result of enormous emissions and unfavorable meteorological conditions. The role of meteorology, particularly extremely unfavorable meteorological events (EUMEs), in processing atmospheric PM2.5 pollution has not been fully addressed. This work examined the variations of PM2.5 mass and its chemical components associated with various meteorological parameters and three EUMEs based on meteorological observations and analysis combined with one-year long in situ measurement in 2018 in the suburban area of Tianjin, China. Analysis shows that the polluted days in 2018 were mostly related to the increase in sulfate, nitrate, and ammonium (SNA). Temperature between -2 to 13 °C is more favorable for the formation of SNA, while high temperature exceeding 28 °C is favorable for the formation of organic carbon and sulfate. Most of the ions and carbon components showed significant increase in concentrations when relative humidity exceeded 80%. The maximum decreasing rate of PM2.5 concentrations due to increase in wind speed and planetary boundary height could be 15.35 µg m-3 (m s-1)-1, and 34.37 µg m-3 (100 m)-1, respectively. EUMEs showed significant impacts on PM2.5 components, in which PM2.5 concentrations showed the most significant increase under temperature inversion (TI) events, and surface-based TI (SBTI) events usually have much stronger impacts on PM2.5 concentrations than elevated TI (ELTI). Nitrate was found to be the most sensitive component to EUMEs, especially under multiple EUMEs. The synthetic effects of multiple EUMEs could result in an increase of nitrate by 35.53 µg m-3 (523.3%). In addition, OC and sulfate are more sensitive to heat wave events. Our analysis provides improved understanding of the formation of PM2.5 pollution with respect to meteorology, particularly EUMEs. Based on such information, more attention may be needed on the collaborative prediction of EUMEs and air pollution episodes.

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The purpose of this study was to make quantitative connections between changes in social and economic activities in northern California urban areas and related Earth system environmental responses to the COVID-19 pandemic in 2020. We tested the hypothesis that the absence of worker activities during Shelter-in-Place in the San Francisco Bay Area detectably altered the infrared heat flux from parking lots, highways, and large building rooftops, caused primarily by quantitative changes in the reflective properties in these different classes of urban surfaces. The Landsat satellite's thermal infrared (TIR) sensor imagery for surface temperature (ST) was quantified for all the large urban features in the Bay Area that have flat (impervious) surfaces, such parking lots, wide roadways, and rooftops. These large impervious surface features in the five-county Bay Area were first delineated and classified using sub-meter aerial imagery from the National Agriculture Imagery Program (NAIP). We then compared Landsat ST data acquired on (or near) the same dates from the three previous years (2017-2019) for all these contiguous impervious surfaces. Results showed that all the large parking lots, roadway corridors, and industrial/commercial rooftops across the entire Bay Area urban landscape were detected by Landsat ST time series as significantly cooler (by 5o C to 8o C) during the unprecedented Shelter-in-Place period of mid-March to late-May of 2020, compared to same months of the three previous years. The explanation for this region-wide cooling pattern in 2020 that was best supported by both remote sensing and ground-based data sets was that relatively low atmospheric aerosol lower (PM2.5) concentrations from mid-March to late May of 2020 resulted in weaker temperature inversions over the Bay Area, higher diurnal surface mixing, and lowered urban surface temperatures, compared to the three previous years.

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Numerous studies have determined the adverse effects of air pollution on the health, welfare, and comfort of humans. More recently, the effects of air pollution on cognitive performance of humans are also focused. However, as a group in direct exposure to air pollution, drivers are rarely studied. The present study aims to evaluate the effect of air pollution on the performance of drivers. Their performance is evaluated by observing the number of recorded accidents. The effect of pollutant concentration (primarily PM2.5) on the number of accidents is investigated in a case study in Tehran. The temperature inversion phenomenon is used as an instrumental variable in a two-stage least squares method. The results indicate that temperature inversion had a significant positive correlation with the concentration of pollutants. Considering temperature inversion as an instrumental variable for these pollutants, it is observed that 1 µg/m3 increase in concentration of PM2.5, increased the number of accidents approximately by 4% in 1 day, for the range of parameters studied. No significant relationship was observed regarding the effect of NO2 and CO on accidents during the study period.

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This present work investigates several local and synoptic meteorological aspects associated with two wintertime haze episodes in Greater Bangkok using observational data, covering synoptic patterns evolution, day-to-day and diurnal variation, dynamic stability, temperature inversion, and back-trajectories. The episodes include an elevated haze event of 16 days (14-29 January 2015) for the first episode and 8 days (19-26 December 2017) for the second episode, together with some days before and after the haze event. Daily PM2.5 was found to be 50 µg m-3 or higher over most of the days during both haze events. These haze events commonly have cold surges as the background synoptic feature to initiate or trigger haze evolution. A cold surge reached the study area before the start of each haze event, causing temperature and relative humidity to drop abruptly initially but then gradually increased as the cold surge weakened or dissipated. Wind speed was relatively high when the cold surge was active. Global radiation was generally modulated by cloud cover, which turns relatively high during each haze event because cold surge induces less cloud. Daytime dynamic stability was generally unstable along the course of each haze event, except being stable at the ending of the second haze event due to a tropical depression. In each haze event, low-level temperature inversion existed, with multiple layers seen in the beginning, effectively suppressing atmospheric dilution. Large-scale subsidence inversion aloft was also persistently present. In both episodes, PM2.5 showed stronger diurnality during the time of elevated haze, as compared to the pre- and post-haze periods. During the first episode, an apparent contrast of PM2.5 diurnality was seen between the first and second parts of the haze event with relatively low afternoon PM2.5 over its first part, but relatively high afternoon PM2.5 over its second part, possibly due to the role of secondary aerosols. PM2.5/PM10 ratio was relatively lower in the first episode because of more impact of biomass burning, which was in general agreement with back-trajectories and active fire hotspots. The second haze event, with little biomass burning in the region, was likely to be caused mainly by local anthropogenic emissions. These findings suggest a need for haze-related policymaking with an integrated approach that accounts for all important emission sectors for both particulate and gaseous precursors of secondary aerosols. Given that cold surges induce an abrupt change in local meteorology, the time window to apply control measures for haze is limited, emphasizing the need for readiness in mitigation responses and early public warning.

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The Sichuan Basin (SB) is one of the four most severely polluted regions in China in terms of air quality, and the frequent generation of temperature inversions is a key factor. The deep mountain-basin topography and the geographical location adjacent to the Tibetan Plateau combine to make the inversion characteristics of this region unique. Knowledge regarding these characteristics remains limited, however. In this study, the radiosonde data at standard pressure levels and significant levels from all SB operational radiosonde stations over 2015-2018 were used to document the climatological features of the inversions from the surface to a height of 5500 m and to evaluate the impact on local air pollutant concentrations. Results revealed that the temperature inversion in the SB is a common and year-round phenomenon. The annual inversion frequency, depth, and strength values are 74.4%, 252.2 m, and 1.3 °C/100 m, respectively. The inversions are most frequent (95.4%), deepest (289.4 m), and strongest (1.6 °C/100 m) in winter. They tend to occur at one of two heights, either below 600 m or between 2200 and 3500 m. Based on their bottom heights, the inversions were divided into three groups: surface-based inversions (SIs), elevated inversions (EIs), and lower-troposphere inversions (LTIs). Annual LTI is most frequent (63.0%) and deepest (264.7 m), while annual SI is strongest (1.8 °C/100 m). Extreme contrasts exist in the seasonal properties of different inversion types. All types of inversions play a considerable role in air pollution, resulting in a high probability of severe and very serious pollution in winter. SI has a greater impact on pollutant concentrations than EI and LTI. The frequent generation of LTIs is a unique feature of the deep SB. LITs exert a significant impact on the formation of local heavy air pollution, but have not been given sufficient attention.

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Low-cost sensors provide an opportunity to improve the spatial and temporal resolution of air quality measurements. Networks of such devices may complement the traditional air quality monitoring and provide some useful information about pollutants and their impact on health. This paper describes the network of 20 nodes for ambient PM2.5 monitoring on a campus area of Wroclaw University of Science and Technology (Wroclaw, Poland). Sensor nodes were equipped with optical sensors PMS A003 (Plantower), which showed high reproducibility between units. The distribution of the sensor nodes was characterised by both high density (14 devices on the main campus area) and wide spread across the city (6 devices on peripheral campuses). During the measurement campaign, signals from sensor nodes were consistent with results from regulatory monitoring stations and sensor devices were capable of indicating elevated levels of PM2.5 concentrations. A great advantage of this system was the ability to provide up-to-date air quality information to the public. Furthermore, air quality messaging was site-specific because of the observed differences in PM2.5 concentrations. Data analysis was aimed at assessing variability between locations using Kendall's τ metric and assessing the statistical significance of the differences in measurement results from neighbouring sensor nodes using the Kolmogorov-Smirnov test. The analysis showed high importance of the nodes in the middle of the main campus and variations of signals from nodes on the peripheries. Differences in signals from sensors located in close proximity to each other were in some cases significant, but only for short-term averaged data. Nevertheless, highly visible variation in PM2.5 signals was observed in the case of nodes arranged vertically on two buildings. PM2.5 concentrations were even 2-4 times greater near the top parts of the buildings than near the ground. The effect of stratification of PM2.5 levels was observed under conditions of temperature inversion.