High Concentrations Of PM2 Research Articles

Aircraft observations on a continuous haze pollution event in Shijiazhuang area

A continuous haze event was recorded on November 14th∼17th, 2020 over Shijiazhuang. Two flights of King-air 350 meteorological research aircraft were performed on November 14th and 16th for the retrieval and observations of meteorological elements, aerosols, and black carbon. In this study, we combined airborne data with air pollution data (PM2.5), ground meteorological data, and ERA-5 reanalysis data to describe the vertical distribution of aerosols (namely 0.1–3.0 μm) and black carbon. We further explicated the formation of this haze event. PM2.5 pollution dominated this haze event, and the highest concentration of PM2.5 was 209 μg/m3. The intensity and height of thermal layers highly linked with the vertical transport of pollution. The highest number concentration of aerosols and black carbon was found below the thermal layers on both airborne sounding days. On the 14th, both BC and aerosol concentrations showed unimodal distribution, and the highest concentrations of BC and aerosols were 12683 ng/m3 and 6965.125#/L at 250 m within layer Ⅰ. The intensity of the thermal layer near-ground was weaker on the 16th that the number concentrations of BC and aerosols also remained at high levels in layer Ⅱ. Backward trajectories of air mass indicated the long-range transport of pollution contributed to the high level of pollution on the 16th. Vapor conditions were more favorable for aerosols growth through moisture absorption. The maximum concentration of 943.58#/L was recorded at particles with a diameter of 0.4 μm on the 16th, while 749.26#/L was reached at 0.14 μm on the 14th. The corresponding height was consistent with the height of maximum concentration in the vertical distribution.

Air Quality Integrated Assessment: Environmental Impacts, Risks and Human Health Hazards

The monitoring and evaluation of air quality is a topic of great global interest as, with the decline of air quality, there are negative effects on human health and ecosystems. Thus, the purpose of this paper was to evaluate the air quality over 11 years, in the period 2011–2021, in four cities in Romania, reported as most polluted, namely, Brasov, Cluj-Napoca, Iasi, and Timisoara. Pollutants of interest included arsenic, carbon monoxide, and PM2.5. The measured concentrations of the selected pollutants were collected from the National Environmental Protection Agency public reports. The database considered the daily measurements for the selected pollutants, from three monitoring stations in each city so that the air quality and trends for the last 11 years and impact assessment could be developed. Therefore, the input data were statistically analyzed to identify the trends of air quality, and then, on this basis, the environmental impacts and risks and health hazards were quantified. High concentrations of PM2.5 were recorded for Iasi city, while for Timisoara city, significant concentrations of arsenic were reported. The results regarding the air quality aggregate index, air pollution index, and health hazard index were in the regular range, but in the case of sensitive, vulnerable targets such as children, they were triple compared to adults. The results show that the alert threshold value for PM2.5 was exceeded every year in all four cities, while in the case of Timisoara city, the arsenic air pollution proved to be at a significant level with a major risk for human health.

Abrupt High PM Concentration in an Urban Calm Cavity Generated by Internal Gravity Waves and a Shallow Coastal Atmospheric Boundary Layer with the Influence of the Yellow Dust from China

Using GRIMM-1107 aerosol sampler, GOES-9 DCD satellite images, HYSPLIT model of backward trajectory and 3D-meteorological WRF-3.6 model, high particulate matter concentrations were investigated at Gangneung city in the Korean east coast which consists of Mt. Taeglyung in its west and the East Sea in its east on 00:00LST March 26~00:00 LST 4 April 2004. During a Yellow Dust period, the maximum PM10 (PM2.5 and PM1) concentration at the city was about 3.3 (1.1 and 1.01) times higher than one in the non-dust period. After the transported dust from the Gobi Desert and Nei-Mongo by strong northwesterly wind passed over Mt. Taegulyang and moved down toward the city. Then the dust was trapped inside a calm cavity generated by the confront of internal gravity waves (IGW) over the city and the eastward movement of the trapped dust is prohibited by the easterly onshore wind from the East Sea, and the trapped dust further combined with particulate and gaseous emitted from the road vehicles and heating boilers of the city at 09:00LST, March 30 (beginning time of office hours), causing high PM concentrations. On mid-day, as the combined dust due to daytime sufficient thermal convection rises up to the top of the thermal internal boundary layer (TIBL) of a 300 m depth from the coast to the top of the mountain, the ground-based PM concentrations in the city are much lower at 15:00LST due to the higher thickness of the TIBL than at 09:00LST. At night, particulates emitted from many road vehicles after the end of office hours and residential heating boilers could combine with both dust transported from the Nei-Mongo and falling dust uplifted from the ground surface of the city during the day, and they were trapped inside a calm cavity by the IGV under much shallower stable nocturnal surface inversion layer than the TIBL, causing more dust to be accumulated near the surface and showing the maximum PM concentrations at 20:00 LST.

SPATIAL AND TEMPORAL QUANTIFICATION OF PARTICULATE MATTER (PM10) AND ASSOCIATED HEAVY METALS IN URBAN ENVIRONMENT OF DELHI

Atmospheric concentrations of PM10 and associated heavy metals were quantified to study the air pollution load of two distinct location of Delhi viz. (1) Agricultural farms of Indian Agricultural Research Institute (IARI) and (2) Yamuna flood plains (YFP). The air sampling has been done for two consecutive years in winter season, comprising the months from November to April in 2020- 21 and 2021-22 respectively. The glass fiber filter paper was used for the particulate matter deposition in high volume sampler. The monthly average concentration of PM10 varies from 112µg/ m3 to 187µg/m3 at IARI and 205 µg/m3 to 364 µg/m3 at YFP. The highest concentration of PM10 was recorded in the month of January. The concentration of PM10 and associated heavy metals were significantly higher at the YFP area. The heavy metal concentration load was 18% and 27% more in the YFP in comparison to the IARI farms in the year 2020-21 and 2021-22 respectively. The higher concentration of heavy metals recorded at YFP because of its location which is cross section of roads having very heavy traffic throughout 24 hrs. and a thermal power plant nearby. The heavy metals concentration trend at each location was in the order of Zn > Fe>Pb>Ni>Cd. The concentration of Pb, Ni, and Cd was significantly less at IARI in comparison to YFP.

A Synoptic- and Remote Sensing-based Analysis of a Severe Dust Storm Event over Central Asia

A severe dust storm blanketing Central Asia on 3–4 November 2021 was investigated employing satellite remote-sensing, synoptic meteorological observations, reanalysis and HYSPLIT back-trajectories. The prevailing meteorological conditions showed an intensification of air subsidence over eastern Kazakhstan, featured in a typical omega-blocking system over the region and two troughs to its west and east axis, one day before the dust storm. The prevailing high-pressure system and temperature gradients over Kazakhstan modulated the dominant anticyclonic wind pattern generated from the south Balkhash basin toward the Caspian Sea, causing a huge dust storm that covered the southern half of Kazakhstan and large parts of Uzbekistan, Tajikistan and Turkmenistan. The dust storm originated in the steppes of southern Kazakhstan by violent downdraft winds. Initially it swept over eastern parts and then the whole of Uzbekistan, reaching the Caspian Sea in the west. Meteorological measurements and HYSPLIT back-trajectories at selected sites in Central Asia (Turkmenabat, Khujand and Tashkent) showed a remarkable dust impact that reduced temperature (by 2–4°C) and visibility to below 1 km at different periods, as the thick dust plume expanded in various directions. The extremely high PM concentrations (PM10 > 10,000 µg m–3 in Tashkent) could endanger both human health and the environment, especially in a region suffering from high susceptibility to wind erosion and significant land degradation and desertification. Effective and immediate stabilising measures to control wind erosion in vulnerable areas of Central Asia are warranted.

Stronger susceptibilities to air pollutants of influenza A than B were identified in subtropical Shenzhen, China

Air pollution was indicated to be a key factor contributing to the aggressive spread of influenza viruses, whereas uncertainty still exists regarding to whether distinctions exist between influenza subtypes. Our study quantified the impact of five air pollutants on influenza subtype outbreaks in Shenzhen, China, a densely populated and highly urbanized megacity. Daily influenza outbreak data of laboratory-confirmed positive cases were obtained from the Shenzhen CDC, from May 1, 2013 to Dec 31, 2015. Concentrations of nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matters ≤2.5 μm (PM2.5), particulate matters ≤10 μm (PM10), and ozone (O3), were retrieved from the 18 national monitoring stations. The generalized additive model (GAM) and distributed lag non-linear model (DLNM) were used to calculate the concentration-response relationships between environmental inducers and outbreak epidemics, respectively for influenza A (Flu-A) and B (Flu-B). There were 1687 positive specimens were confirmed during the study period. The cold season was restricted from Nov. 4th to Apr. 20th, covering all seasons other than the long-lasting summer. Relatively heavy fine particle matter (PM2.5) and NO2 pollution was observed in cold months, with mean concentrations of 46.06 μg/m3 and 40.03 μg/m3, respectively. Time-series analysis indicated that high concentrations of NO2, PM2.5, PM10, and O3 were associated with more influenza outbreaks at short lag periods (0–5 d). Although more Flu-B (679 cases) epidemics occurred than Flu-A (382 cases) in the cold season, Flu-A generally showed higher susceptibility to air pollutants. A 10 μg/m3 increment in concentrations of PM2.5, PM10, and O3 at lag 04, was associated with a 2.103 (95%CI: 1.528–2.893), 1.618 (95%CI: 1.311–1.996), and 1.569 (95%CI: 1.214–2.028) of the relative risk (RR) of Flu-A, respectively. A 5 μg/m3 increase in NO2 was associated with higher risk of Flu-A at lag 03 (RR = 1.646, 95%CI: 1.295–2.092) and of Flu-B at lag 04 (RR = 1.319, 95%CI: 1.095–1.588). Nevertheless, barely significant effect of particulate matters (PM2.5, PM10) on Flu-B and SO2 on both subtypes was detected. Further, the effect estimates of NO2 increased for both subtypes when coexisting with other pollutants. This study provides evidence that declining concentrations of main pollutants including NO2, O3, and particulate matters, could substantially decrease influenza risk in subtropical Shenzhen, especially for influenza A.

Residential exposure to livestock farms and lung function in adolescence – The PIAMA birth cohort study

BackgroundThere is a growing interest in the impact of air pollution from livestock farming on respiratory health. Studies in adults suggest adverse effects of livestock farm emissions on lung function, but so far, studies involving children and adolescents are lacking. ObjectivesTo study the association of residential proximity to livestock farms and modelled particulate matter ≤10 μm (PM10) from livestock farms with lung function in adolescence. MethodsWe performed a cross-sectional study among 715 participants of the Dutch prospective PIAMA (Prevention and Incidence of Asthma and Mite Allergy) birth cohort study. Relationships of different indicators of residential livestock farming exposure (distance to farms, distance-weighted number of farms, cattle, pigs, poultry, horses and goats within 3 km; modelled atmospheric PM10 concentrations from livestock farms) with forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) at age 16 were assessed by linear regression taking into account potential confounders. Associations were expressed per interquartile range increase in exposure. ResultsHigher exposure to livestock farming was consistently associated with a lower FEV1, but not with FVC among participants living in less urbanized municipalities (<1500 addresses/km2, N = 402). Shorter distances of homes to livestock farms were associated with a 1.4% (0.2%; 2.7%) lower FEV1. Larger numbers of farms within 3 km and higher concentrations of PM10 from livestock farming were associated with a 1.8% (0.8%, 2.9%) and 0.9% (0.4%,1.5%) lower FEV1, respectively. ConclusionsOur findings suggest that higher exposure to livestock farming is associated with a lower FEV1 in adolescents. Replication and more research on the etiologic agents involved in these associations and the underlying mechanisms is needed.

Time Trends of Greenspaces, Air Pollution, and Asthma Prevalence among Children and Adolescents in India.

The prevalence of childhood asthma contributes to the global burden of the disease substantially. Air pollution in India has increased. In this study, we examine the associations among greenspaces, air pollution, and asthma prevalence in children and adolescents over a large, diverse population in India. We used state-wide global burden of disease data on asthma from age 0 to 19 years in 2005, 2011, and 2017. For greenspace, we used the normalized differential vegetation index (NDVI), which is the surface reflectance of light during photosynthetic activity. NDVI, air pollutants (PM2.5, PM10, SO2, NO2, and O3), weather, and socio-demographic factors were included in generalized estimating equation (GEE) models to estimate their associations with childhood asthma prevalence over time. Novel data visualization illustrated the complex spatial distributions. NDVI was associated with asthma prevalence (β = 0.144; 95% CI = 0.10, 0.186; p &lt; 0.0001) for high PM2.5, along with high levels of both gaseous air pollutants, SO2, and NO2 ((β = 0.12; 95% CI = 0.08, 0.16; p &lt; 0.0001) and (β = 0.09; 95% CI = 0.05, 0.13; p &lt; 0.0001)). However, NDVI and high O3, had a strong negative association with asthma prevalence (β = -0.19; 95% CI = -0.26, -0.11; p &lt; 0.0001). We observed additional effects of the interaction between the NDVI and high concentrations of PM2.5, PM10, NO2, and O3, assuming that these associations share a common pathway, and found interaction effects for asthma prevalence. Given the changing environmental conditions that interplay over geographical characteristics on the prevalence of asthma, further studies may elucidate a better understanding of these complex associations.

Neighborhood environmental factors linked to hospitalizations of older people for viral lower respiratory tract infections in Spain: a case-crossover study

BackgroundLower respiratory tract viral infection (LRTI) is a significant cause of morbidity-mortality in older people worldwide. We analyzed the association between short-term exposure to environmental factors (climatic factors and outdoor air pollution) and hospital admissions with a viral LRTI diagnosis in older adults.MethodsWe conducted a bidirectional case-crossover study in 6367 patients over 65 years of age with viral LRTI and residential zip code in the Spanish Minimum Basic Data Set. Spain’s State Meteorological Agency was the source of environmental data. Associations were assessed using conditional logistic regression. P-values were corrected for false discovery rate (q-values).ResultsAlmost all were hospital emergency admissions (98.13%), 18.64% were admitted to the intensive care unit (ICU), and 7.44% died. The most frequent clinical discharge diagnosis was influenza (90.25%). LRTI hospital admissions were more frequent when there were lower values of temperature and O3 and higher values of relative humidity and NO2. The regression analysis adjusted by temperatures and relative humidity showed higher concentrations at the hospital admission for NO2 [compared to the lag time of 1-week (q-value< 0.001) and 2-weeks (q-value< 0.001)] and O3 [compared to the lag time of 3-days (q-value< 0.001), 1-week (q-value< 0.001), and 2-weeks (q-value< 0.001)] were related to a higher odds of hospital admissions due to viral LRTI. Moreover, higher concentrations of PM10 at the lag time of 1-week (q-value = 0.023) and 2-weeks (q-value = 0.002), and CO at the lag time of 3-days (q-value = 0.023), 1-week (q-value< 0.001) and 2-weeks (q-value< 0.001)], compared to the day of hospitalization, were related to a higher chances of hospital admissions with viral LRTI.ConclusionUnfavorable environmental factors (low temperatures, high relative humidity, and high concentrations of NO2, O3, PM10, and CO) increased the odds of hospital admissions with viral LRTI among older people, indicating they are potentially vulnerable to these environmental factors.Graphical

Dependence Analysis of PM2.5 Concentrations in 295 Chinese Cities in the Winter of 2019–2020

Considering the current severe atmospheric pollution problems in China, a comprehensive understanding of the distribution and spatial variability of PM2.5 is critically important for controlling pollution and improving the future atmospheric environment. This study first explored the distribution of PM2.5 concentrations in China, and then developed a methodology of “dependence analysis” to investigate the relationship of PM2.5 in different cities in China. The data of daily PM2.5 concentrations were collected from the environmental monitoring stations in 295 cities in China. This study also developed a set of procedures to evaluate the spatial dependence of PM2.5 among the 295 Chinese cities. The results showed that there was a total of 154 city pairs with dependence type “11”, under a significance level of 0.5%. Dependence type “11” mainly occurred between nearby cities, and the distance between 89.0% of the dependent city pairs was less than 200 km. Furthermore, the dependent pairs mainly clustered in the North China Plain, the Northeast Plain, the Middle and Lower Yangtze Plain and the Fen-Wei Plain. The geographic conditions of the Plain areas were more conducive to the spread of PM2.5 contaminants, while the mountain topography was unfavorable for the formation of PM2.5 dependencies. The dependent city couples with distances greater than 200 km were all located within the Plain areas. The high concentration of PM2.5 did not necessarily lead to PM2.5 dependences between city pairs. The methodology and models developed in this study will help explain the concentration distributions and spatial dependence of the main atmospheric pollutants in China, providing guidance for the prevention of large-scale air pollution, and the improvement of the future atmospheric environment.

The association of long-term ambient fine particulate matter exposure with blood pressure among Chinese adults

Previous studies indicated that long-term exposure to high level of fine particulate matter (PM2.5) was associated with elevated blood pressure (BP) and hypertension, but most of them were conducted in high-income countries with low PM2.5 level. Therefore, we aimed to evaluate the adverse impacts of long-term exposure to PM2.5 on BP and hypertension in China with high concentration. A total of 99,084 adults aged ≥18 years old were included from three cohorts among the project of Prediction for Atherosclerotic Cardiovascular Disease Risk in China. PM2.5 concentrations during 2000–2015 at 1 × 1 km spatial resolution were evaluated using satellite-based spatiotemporal models. Generalized estimating equation was applied to assess the impact of three-year average PM2.5 concentrations on BP level and hypertension. We also examined whether health status and lifestyles modified the effects of PM2.5 on BP and hypertension. Generally, high concentration of PM2.5 was associated with increased BP level and higher risk of hypertension. With each 10 μg/m3 increment in PM2.5 concentration, systolic BP (SBP) and diastolic BP (DBP) increased by 1.67 [95% confidence interval (CI): 1.48, 1.86] mmHg and 0.45 (95% CI: 0.35, 0.56) mmHg, and the prevalence of hypertension increased by 29% [odds ratio (OR): 1.29, 95% CI: 1.26, 1.32]. In comparison with the first quartile of PM2.5 concentration, SBP, DBP and prevalence of hypertension in the fourth quartile were increased by 8.26 (95% CI: 7.73, 8.80) mmHg, 2.85 (95% CI: 2.55, 3.15) mmHg, and 133% (OR: 2.33, 95% CI: 2.21, 2.47), respectively, in the fully adjusted model. However, the relationships of PM2.5 with BP might be non-linear, as BP level started to decline when PM2.5 exceeded 75 μg/m3. In conclusion, long-term PM2.5 exposure could elevate BP level and prevalence of hypertension. People living in high-polluted areas should strengthen their awareness of prevention.

Evaluating the Impact of Vehicular Aerosol Emissions on Particulate Matter (PM2.5) Formation Using Modeling Study

Automobile emissions in urban cities, such as Peru, are significant; however, there are no published studies of the effects of these emissions on PM2.5 (fine particulate matter) formation. This study aims to analyze the contributions of vehicle aerosol emissions to the surface mass concentration of PM2.5 in the Metropolitan Area of Lima and Callao (MALC), one of the most polluted cities in Latin America and the Caribbean (LAC) known to have high concentrations of PM2.5. In February 2018, we performed two numerical simulations (control and sensitivity) using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). We considered both trace gasses and aerosol emissions from on-road traffic for the baseline simulation (hereinafter referred to as “control”); gasses without particulate emissions from vehicles were considered for the sensitivity simulation (hereinafter referred to as WithoutAerosol). For control, the model’s performance was evaluated using in situ on-ground PM2.5 observations. The results of the predicted PM2.5 concentration, temperature, and relative humidity at 2 m, with wind velocity at 10 m, indicated the accuracy of the model for the control scenario. The results for the WithoutAerosol scenario indicated that the contributions of vehicular trace gasses to secondary aerosols PM2.5 concentrations was 12.7%; aerosol emissions from road traffic contributed to the direct emissions of fine aerosol (31.7 ± 22.6 µg/m3).

PM10 aerosol enhancement in the anticyclonic anomalies caused by the East Asian spring warming of 2021

This study analyzed the PM10 aerosol enhancement caused by East Asian atmospheric warming during transboundary transport in March of 2021. It was found to be cooled near the north polar area and to have shifted the polar vortices northward over the East Asian region in the mid-troposphere. The Siberian high was weakened and the East Asian region was characterized by a positive air temperature anomaly and negative zonal wind speeds. The increasing rate of air temperature anomalies at 2 m was higher in the high latitude areas over 40°N in Mongolia and northeastern China, causing a decrease in zonal wind speeds at 10 m over eastern China and Korea. The anomalous variations of the MODIS-induced aerosol optical depth (AOD) were found to be positive with the highest value of +0.93 in the area ranging from northeastern China to Korea, despite the recent decreasing trends. The positive anomalous region of the MODIS-induced AOD was correlated with positive air temperatures and negative zonal wind speeds, causing enhanced formations of secondary aerosols in the atmosphere. As cold northerly winds were suppressed in the East Asian region, anomalous high pressures (anticyclonic anomalies) developed frequently in eastern China and moved to Korea. The Weather Research and Forecasting model to which meteorological and climate data were applied coupled with Chemistry (WRF-Chem) simulations on increases in the PM10 enhancement ratio, indicating enhanced formation amounts, during transboundary transport for the period of March 13–15, 2021. An air mass near the high terrain surface over eastern China under the backward side of an anticyclone warmed and moved toward the relatively low terrain of the Yellow Sea and Korea in keeping with the height. Air temperature inversion developed in the anomalous warmer center in the mid-troposphere, reaching the surface of the Yellow Sea and Korea. High PM10 concentrations were constrained near the shallow inversion layer. Furthermore, the PM10 enhancement ratio showed a positive level over the Yellow Sea with nearly zero anthropogenic emissions on the surface, gradually increasing at a rate of approximately 60% in Korea. The hourly contribution by the warming-induced PM10 enhancement (PM10 CbWIE) was calculated and found to be at the highest level of 70% in Seoul, Korea. Among PM10 secondary inorganic aerosols, nitrate formations were enhanced during transboundary transport in the warming-induced anticyclone with reactions rich in ammonium. While the sulfate enhancement ratio reached a positive level over the Yellow Sea, the gradual decrease during transboundary transport may be reflected by the decreased emissions of sulfur dioxide in eastern China.

Assessment of children’s exposure to air pollutants in urban residences during the COVID-19 pandemic

Indoor air pollution has been associated with adverse health effects. Sensitive people such as children spend much time at home. It is therefore important to know whether our children breath clean air inside their residences or not. Monitoring of PM10, PM2.5, TVOCs, CO, and CO2 concentrations was conducted for 24 h in 36 urban residences in Alexandria—Egypt, simultaneously outdoors and indoors (living rooms, bedrooms, and kitchens), during the summer season. During the COVID-19 pandemic, children spend 98% of their time at home (49% in living rooms, 47% in bedrooms, and 2% in kitchens). Results indicated that children are particularly exposed to high concentrations of PM10 (85.4 ± 18.7 μg/m3) and PM2.5 (57.2 ± 16.4 μg/m3) exceeding the WHO guidelines, which can be dangerous for their health. Outdoor sources were found to be a main contributor to the indoor levels of PM10, PM2.5, and CO in bedrooms and living rooms of the surveyed residences. Therefore, effective control measures to reduce outdoor air pollution can result in improved indoor air quality (IAQ). Living rooms showed the highest impact on the daily average children exposure to PM2.5, PM10, TVOCs, and CO with average contributions of 59%, 56%, 57%, and 61%, respectively. For CO2, bedrooms showed the largest contribution to the children’s average exposure with an average value of 49%, pointing to inadequate ventilation and small size of this type of room. Indoor occupants’ activities were found to considerably affect IAQ in the bedrooms and living rooms. The study also highlighted the importance of other determinants, such as occupancy and air exchange rate, on IAQ in these two microenvironments. This research provides the first data on children’s exposure to indoor air pollutants in urban residences in Egypt.

Evaluation of Air Quality in Sudanese Gold Mining Fields

This study intended to provide a base line and describe the current air quality in Sudanese Gold mining fields. It covers 21 gold marketing centers in 5 states; namely Northern, River Nile, Gadaref, Red Sea and Kurdofan state. The main objectives of the survey is the detection of any significant concentrations that exceed Occupational Safety and Health norms and specifying the key emission sources, and also identification and assessing of the health hazards and environmental impacts. The tests were carried out by using electronic portable devices to measure the concentrations of pollutants in air. The measured pollutants were: Dust or Particulate Matter (PM), Nitrogen Dioxide (NO2), Volatile Organic Components (VOCs), Sulphur Dioxide (SO2), Ammonia (NH3), Carbon Monoxide (CO) and Ozone (O3). National Ambient Air Quality Standards (NAAQSs) and Sudanese Standards for Ambient Air Pollutants were used for comparison purposes. In addition to that, Air Quality Index (AQI) was applied for the average concentrations of each pollutant. It was found that, all gold marketing centers suffered from very high concentration of PM2.5 and PM10. 90.5% of those centers had higher NO2 concentration than the standards, 37% of it was increased by 200% and 42% of it was increased by 150%. In addition to that, 48% of gold marketing centers had higher SO2 concentration than the standards. On the other hand, concentrations of other pollutants obtained were found within the allowable limits. Finally, it was concluded that all those centers are contaminated with one or more air pollutants. Therefore, it was recommended to improve the environmental conditions by mitigating emissions from point sources. Supervisors have been advised to assume their responsibilities in controlling the use of Personal Protective Equipment (PPE) in addition to the dissemination of safety culture through psychological approach.

Estimating PM2.5 Concentrations Using the Machine Learning RF-XGBoost Model in Guanzhong Urban Agglomeration, China

Fine particulate matter (PM2.5) is a major pollutant in Guanzhong Urban Agglomeration (GUA) during the winter, and GUA is one of China’s regions with the highest concentrations of PM2.5. Daily surface PM2.5 maps with a spatial resolution of 1 km × 1 km can aid in the control of PM2.5 pollution. Thus, the Random Forest and eXtreme Gradient Boosting (RF-XGBoost) model was proposed to fill the missing aerosol optical depth (AOD) at the station scale before accurately estimating ground-level PM2.5 using the recently released MODIS AOD product derived from Multi-Angle Implementation of Atmospheric Correction (MAIAC), high density meteorological and topographic conditions, land-use, population density, and air pollutions. The RF-XGBoost model was evaluated using an out-of-sample test, revealing excellent performance with a coefficient of determination (R2) of 0.93, root-mean-square error (RMSE) of 12.49 μg/m3, and mean absolution error (MAE) of 8.42 μg/m3. The result derived from the RF-XGBoost model indicates that the GUA had the most severe pollution in the winter of 2018 and 2019, owing to the burning of coal for heating and unfavorable meteorological circumstances. Over 90% of the GUA had an annual average PM2.5 concentrations decrease of 3 to 7 μg/m3 in 2019 compared to the previous year. Nevertheless, the air pollution situation remained grim in the winter of 2019, with more than 65% of the study area meeting the mean PM2.5 values higher than 35 μg/m3 and the maximum reaching 95.57 μg/m3. This research would be valuable for policymakers, environmentalists, and epidemiologists, especially in urban areas.

An analysis of particulate pollution using urban aerosol pollution island intensity over Delhi, India.

The accent of the present study is determination of Urban Aerosol Pollution Island (UAPI) intensity and spatial variability in particulate matter concentration (PM10 and PM2.5) over Delhi. For analysis, the hourly concentration dataset of PM2.5 and PM10 from January 2019 to December 2020 was obtained from ten air quality monitoring stations of Delhi. Additionally, UAPI Index has been calculated to assess the intensity of particulate pollution. The daily, monthly, and annual variations in the trends of PM10, PM2.5, and UAPI index along with related meteorological parameters have been analyzed. Particulate pollution peaked majorly during two seasons, i.e., summer and winter. The highest concentration of PM10 was observed to be 426.77 µg/m3 while that of PM2.5 was observed to be 301.91 µg/m3 in January 2019 for traffic-affected regions. During winters, higher PM2.5 concentration was observed which can be ascribed to increased local emissions and enhanced secondary particle formations. While the increase in PM10 concentrations led to an increment in pollution episodes during summers over most of the sites in Delhi. The UAPI index was found to be declining in 2020 over traffic affected regions (77.92 and 27.22 for 2019 and 2020, respectively) as well as in the background regions (64.91 and 19.80 for 2019 and 2020, respectively) of Delhi. Low traffic intensity and reduced pollutant emission could have been responsible for the reduction of UAPI intensity in the year 2020. The result indicates that lockdown implemented to control the COVID-19 outbreak led to an unexpected decrease in the PM10 pollution over Delhi.

PREDICTION OF FINE PARTICULATE MATTER IN LOW-EMISSION ZONES USING A MODIFIED NUMERICAL METHOD FOR A SYSTEM OF ORDINARY DIFFERENTIAL EQUATIONS

Currently, many European cities have severely exceeded the EU air quality standards and are struggling with high concentrations of fine particulate matter PM10 and PM2.5 in the air, with road transport often being one of the major polluters. One of the forms for correction of the problem that many cities in the EU are currently using is the construction of low-emission zones. For the prediction of PM10 and PM2.5, a modified numerical method for a system of ordinary differential equations has been proposed. In the right part of this system, in addition to the main trend and the periodicity of PM10 and PM2.5, their correlation is taken into account. Against the background of the best solution obtained, a forecast is made for the emission levels in a period of one week in the town of Ruse.

Does racism have inertia? A study of historic redlining’s impact on present-day associations between development and air pollution in US cities

We explore how Home Owners’ Loan Corporation (HOLC) scores of the 1930s impact 2010 and 2015 inhalable particulate matter (PM10) concentrations for 15,232 census tracts, clustered in 196 cities throughout the contiguous United States. Using areal apportionment, we assign a HOLC score to housing tracts and construct hierarchical linear models to examine the relationship between the policy practice of redlining, PM pollution, and urban economic development. We find that redlining is associated with higher PM10 concentrations, and that higher HOLC grades also intensify the association of per capita income, median rent, median home values, and racial composition with PM10. These findings suggest that historical policy programs that were grounded in racial logics—such as the HOLC practice of ‘redlining’—have an inertia that results in them influencing development pathways and environmental outcomes of built environments for decades.

Particulate Matter Concentrations and Fungal Aerosol in Horse Stables as Potential Causal Agents in Recurrent Airway Disease in Horses and Human Asthma and Allergies

Exposure to bioaerosols associated with horse stable indoor environment and their health effects on people and horses has recently become of particular interest. Moreover, increasing frequency of recurrent airway disease (RAO) among horses made it necessary to search for the most probable causal agents of this disease and methods of their eradication. The study was conducted in two horse stables in southern Poland (Kraków and Tarnów). Particulate matter (PM2.5, PM4, and PM10) concentrations were determined photometrically, the concentration of fungal aerosol was determined by a six-stage impactor, and next generation sequencing (NGS) was used to determine fungal community composition in one of these stables. The highest PM concentrations were observed in Tarnów, but fungal aerosol levels were higher in the Kraków stable. Based on the NGS results, the three most prevalent fungal species were Wallemia sebi, Aspergillus penicillioides, and Epicoccum nigrum, all highly allergenic and potentially involved in the occurrence of RAO in horses. Spores of the detected fungi can penetrate deeply into the respiratory system. Therefore, this study suggests that examinations of particulate matter and fungal aerosol concentrations, along with species composition assessment, should be regularly conducted in horse stables.