Average Concentrations Of PM2 Research Articles

Association of Short-Term Co-Exposure to Particulate Matter and Ozone with Mortality Risk.

A complex regional air pollution problem dominated by particulate matter (PM) and ozone (O3) needs drastic attention since the levels of O3 and PM are not decreasing in many parts of the world. Limited evidence is currently available regarding the association between co-exposure to PM and O3 and mortality. A multicounty time-series study was used to investigate the associations of short-term exposure to PM1, PM2.5, PM10, and O3 with daily mortality from different causes, which was based on data obtained from the Mortality Surveillance System managed by the Jiangsu Province Center for Disease Control and Prevention of China and analyzed via overdispersed generalized additive models with random-effects meta-analysis. We investigated the interactions of PM and O3 on daily mortality and calculated the mortality fractions attributable to PM and O3. Our results showed that PM1 is more strongly associated with daily mortality than PM2.5, PM10, and O3, and percent increases in daily all-cause nonaccidental, cardiovascular, and respiratory mortality were 1.37% (95% confidence interval (CI), 1.22-1.52%), 1.44% (95% CI, 1.25-1.63%), and 1.63% (95% CI, 1.25-2.01%), respectively, for a 10 μg/m3 increase in the 2 day average PM1 concentration. We found multiplicative and additive interactions of short-term co-exposure to PM and O3 on daily mortality. The risk of mortality was greatest among those with higher levels of exposure to both PM (especially PM1) and O3. Moreover, excess total and cardiovascular mortality due to PM1 exposure is highest in populations with higher O3 exposure levels. Our results highlight the importance of the collaborative governance of PM and O3, providing a scientific foundation for pertinent standards and regulatory interventions.

Indoor Environmental Quality in Micro & Nanoelectronics Laboratories at IMEN, Research Complex, UKM

The National University of Malaysia's Micro and Nanoelectronics laboratories were examined to ascertain their comfort levels. Eight laboratories were chosen as the study locations. This study aims to assess the interior air quality at six locations by measuring CO2, formaldehyde CHOH, total volatile organic compound TVOC, PM2.5, indoor temperature, and relative humidity. The thermal comfort evaluation for labs 1, 2, 3, 4, 5, and 7 is between 20.45 and 22.47 °C, which is below the range of DOSH and Ashrae. While the average readings in laboratories 6 and 8 are within the limits allowed at 23,21 °C and 23,75 °C, respectively. Five laboratories were exceeding the upper limit of RH (30–60%) but only three laboratories were still within the maximum level mentioned by the Ashrae standard. RH exceeded the ICOP limit (60%) at 70.3%, 63.7%, 75.4%, 60.6%, 62.8% and 65.5%, at Lab 1, 2,3,6,7 and 8, respectively, whereas temperature exceeded it (22.5- 26 °C) at 20.45, 22.2, 21.9, 22.43, 21.58 and 22.47 at Lab 1, 2, 3, 4, 5, 7 and 8, respectively. While the average air velocities for all laboratories are 0.17, 0.13, 0.10, 0.16, 0.12, 0.14, 0.15 and 0.09 ms-1, respectively. All the data that has been measured is found to be below the maximum level as recommended by the ASHRAE Standard 55 (2004) of 0.25 ms-1. The indoor air contaminants (CO, CHOH, TVOC, PM2.5, and PM10) met the standard level of ICOP and DOSH except for CHOH approaches ICOP (0.1ppm) at 0.1 and 0.09, at Point 9 and Point 10 for laboratory 3 compared to other laboratories where the concentration obtained is lower. The highest laboratories average concentration of PM2.5 was 13 µg/m3 determined in Lab 3, which was the most actively utilized one because the research activity in this laboratory was working more intensively than the others. As in the study of PM2.5, the highest average PM10 concentration was evaluated in Lab 3 as 36 µg/m3. Based on observations and studies, we find that fresh outdoor air with a complete and good purifier/filter should be used to reduce the concentration of indoor pollutants.

Job Exposure Matrix, a Solution for Retrospective Assessment of Particle Exposure in a Subway Network and Their Long-Term Effects.

Health effects after long-term exposure to subway particulate matter (PM) remain unknown due to the lack of individual PM exposure data. This study aimed to apply the job exposure matrix (JEM) approach to retrospectively assess occupational exposure to PM in the Parisian subway. Job, the line and sector of the transport network, as well as calendar period were four JEM dimensions. For each combination of these dimensions, we generated statistical models to estimate the annual average PM10 concentration using data from an exhaustive inventory of the PM measurement campaigns conducted between 2004 and 2020 in the Parisian subway and historical data from the Parisian air pollution monitoring network. The resulting JEM and its exposure estimates were critically examined by experts using the uncertainty analysis framework. The resulting JEM allows for the assignment of the estimated annual PM10 concentration to three types of professionals working in the subway: locomotive operators, station agents, and security guards. The estimates' precision and validity depend on the amount and quality of PM10 measurement data used in the job-, line-, and sector-specific models. Models using large amounts of personal exposure measurement data produced rather robust exposure estimates compared to models with lacunary data (i.e., in security guards). The analysis of uncertainty around the exposure estimates allows for the identification of the sources of uncertainty and parameters to be addressed in the future in order to refine and/or improve the JEM. The JEM approach seems relevant for the retrospective exposure assessment of subway workers. When applied to available data on PM10, it allows for the estimation of this exposure in locomotive operators and station agents with an acceptable validity. Conversely, for security guards, the current estimates have insufficient validity to recommend their use in an epidemiological study. Therefore, the current JEM should be considered as a valid prototype, which shall be further improved using more robust measurements for some jobs. This JEM can also be further refined by considering additional exposure determinants.

Short‐term estimations of PM10 concentration in the Middle Black Sea region based on grey prediction models

AbstractThe Middle Black Sea region has experienced severe air pollution, with a significant increase in particulate matter (PM) concentration due to a growth in population, financial activity, and an expansion of transportation in recent years. Therefore, the prediction of PM concentration has become a topic of great significance to reduce air pollution and assess the effects on public health. In this study, the grey prediction model (GM (1,1)), the discrete grey model (DGM (1,1)), and the grey Verhulst model (GVM (1,1)) were used to estimate the PM10 concentration of the cities Amasya, Çorum, Ordu, and Samsun in the Middle Black Sea region, for the period from 2022 to 2026. The accuracy of the GM (1,1), DGM (1,1), and GVM (1,1) models in fitting data was calculated using the mean absolute percentage error (MAPE) value. Since three types of prediction models of MAPEs were less than 20%, they were considered a good value for prediction performance. Furthermore, the results showed that the PM10 concentrations of Amasya, Çorum, and Ordu showed a downward trend over the next 5 years. However, the GVM (1,1) model showed an upward trend in the yearly average PM10 concentration in Samsun. In conclusion, these models could be considered a reliable approach in early warning systems for emissions reduction and as a long‐term policy for managing air quality in the Middle Black Sea region.

Experimental Investigation on PM10 and PM2.5 Concentrations in North Bandung

North Bandung is an area where there are several tourist attractions, including the Dago Dream Park. The main road to several locations in North Bandung is via the road in front of Terminal Dago. For this reason, the purpose of this study was to collect data on PM2.5 and PM10 concentrations at both locations, namely in front of Terminal Dago and Dago Dream Park. Data collection was carried out for seven days, from Monday to Sunday, from 08.00 to 16.00 local time. To evaluate air quality in these two places, the national standard, namely PPRI No. 22 of 2021 and international standards from WHO were applied. The measurement results show that the concentrations of PM2.5 and PM10 in these two locations are still within the national standard. Meanwhile, when evaluated with WHO standards, PM10 concentrations in both locations are still within standard, but for PM2.5, there are several days where the air quality is out of the standard. The average concentrations of PM2.5 and PM10 during the 7 days of measurement at Terminal Dago are 19.9 μg/m3 and 21.6 μg/m3, respectively. While the average concentration of PM2.5 and PM10 during 1 week of data collection were 18.9 μg/m3 and 19.9 μg/m3, respectively. This means that the concentration of PM2.5 and PM10 at Terminal Dago is slightly higher than that of at Dago Dream Park. In addition, based on an evaluation using national standards, Dago Dream Park tourist attractions still have good air quality and are safe for local residents and tourists. From the data recorded at the Health Centre (Puskesmas), the number of ARI cases in the two locations in 2020 and 2021 is relatively not much different.

Physicochemical characteristics of atmospheric particles in south western (Pune) region of India: Elemental size distribution, source analysis and risk assessment

Chemical characteristics of size-resolved particulate matter (PM) and bound elemental species were investigated under an intensive measurement campaign during winter of 2021 in urban dwelling of south western (Pune) region of India. During the study period, average mass concentrations of PM10, PM4, PM2.5 and PM1 were 182.4 ± 48.7 μgm−3, 76.2 ± 26.2 μgm−3, 63.5 ± 22.4 μgm−3, 17.7 ± 6.9 μgm−3 respectively. Diurnal variability indicated significant high concentration during night and early morning hours attributed to the high volume of heavy-duty vehicles. Bimodal distributions of PM mass concentrations in coarse fraction (Mass Median Aerodynamic Diameter (MMAD) ± Geometric Standard Deviation (GSD) = 4.32 ± 1.27) was noted while accumulation of elements was found higher in fine(0.43–2.1 μm) > coarse(2.1–5.8 μm) > large mode(5.8–10 μm) particles. As per size distribution characteristics, Mn, Pb, Cu, Sn, Zn, Mo were concentrated in coarse mode; As, Cr, Fe, Ni was concentrated in both large and coarse mode while Al, Si depicted multimodal distribution across coarse and fine range. High MMAD (≥1.8) and GSD (>1.2) of all elements pointed towards significance of coarse particles in this study region. Ni and Cr (VI) via inhalation exposure pathway pose highest hazard quotient (HQ) and excess lifetime cancer risk (ELCR) respectively within respirable size range(2.1–3.3 μm) while children were subject to potential health risk than adults mainly through ingestion mode of exposure. Principal Component Analysis (PCA) suggested crustal origin, resuspended road dust along with vehicular traffic and industrial emissions as major sources.

Prediction of daily average PM10 concentrations using feedforward neural network in Kocaeli, northwestern Türkiye

Prediction of daily average PM10 concentrations using feedforward neural network in Kocaeli, northwestern Türkiye

Associations between long-term exposure to PM2.5 chemical constituents and allergic diseases: evidence from a large cohort study in China

BackgroundExposure to air pollutants may cause immune responses and further allergic diseases, but existing studies have mostly, if not all, focused on effects of short-term exposure to PM2.5 on allergic diseases. ObjectivesWe estimated associations of long-term exposure to PM2.5 chemical constituents with allergic disease risks and effect modification. MethodsWe used the baseline of a newly established, provincially representative cohort of 51,480 participants in southwest China. The presence of allergic rhinitis, allergic asthma, urticaria, and allergic conjunctivitis was self-reported by following a formed questionnaire in face-to-face interviews. The average concentrations of PM2.5 chemical constituents (NO3−, SO42−, NH4+, organic matter [OM], and black carbon [BC]) over participants' residence were estimated using machine learning models. Logistic regression with double robust estimator and weighted quantile sum regression were used to estimate the effects of PM2.5 chemical constituents on allergic disease risks, as well as relative importance of each PM2.5 chemical constituent. ResultsPer interquartile range increase in the concentration of all PM2.5 chemical constituents was associated with the elevated risks for allergic asthma (OR = 1.79 [1.41–2.26]), allergic conjunctivitis (1.54 [1.19–2.00]), urticaria (1.36 [1.25–1.48]), and allergic rhinitis (1.18 [1.11–1.26]). NO3− contributed more to risks for allergic asthma (weight = 46.05 %), urticaria (72.29 %), and allergic conjunctivitis (47.65 %), while NH4+ contributed more to allergic rhinitis (78.07 %). OM contributed most to the risks for allergic asthma (30.81 %) and allergic conjunctivitis (31.40 %). BC was also associated with allergic rhinitis, urticaria, and allergic conjunctivitis, only with a considerable weight for urticaria (24.59 %). Joint effects of PM2.5 chemical constituents on risks for allergic rhinitis and urticaria were stronger in minorities and farmers than their counterparts. ConclusionLong-term exposure to PM2.5 chemical constituents was associated with the increased allergic disease risks, with NO3− and NH4+ accounting for the largest variance of the associations. Our findings would serve as scientific evidence for developing more explicit strategies of air pollution control.

Sebaran Particulate Matter (PM10, PM2,5, PM1, PM0,1) di SMP Negeri 1 Kota Jambi Menggunakan Model CFD (Computational Fluid Dynamics)

One source that contributes greatly to urban air quality is traffic. The proximity of schools to vehicles will put students at greater risk of exposure to high concentrations of particulate matter. SMP Negeri 1 Jambi City is an education center located in an urban area and adjacent to a busy road. CFD models are good at modeling the movement of pollutants in urban areas by taking into account the influence of buildings. The purpose of this study is to determine the concentration of PM10, PM2.5, PM1, and PM0.1 at SMPN 1 Jambi City based on direct measurement results and CFD modeling results and then see how accurate the modeled PM concentrations are when compared to the results of direct measurements. The results showed that the average concentrations of PM10, PM2.5, PM1, and PM0.1 from direct measurements were 20.66 µg/m3, 11.79 µg/m3, 8.74 µg/m3, and 1.96 µg/m3, respectively, while the modeling results showed lower average concentrations. The ratio of the difference between the measured and modeled PM concentrations is in the range of 11.67 - 233.45% and with the percentage of RMSPE obtained >30% (invalid), where the requirement for a modeling to be valid so that the results can be trusted in explaining the actual phenomenon is when the validity percentage is <30%.

Evaluation of Toxic Heavy Metals and Health Risk in Airborne Particulate Matter at Qassim region, Saudi Arabia

Particulate matter (PM) significantly influences air quality, visibility, climate change, the earth’s radiative balance, and human health. The significance of carrying out this investigation in its entirety is due to the small amount of data that has been collected and published in Qassim region (26.2078º N, 43.4837º E). Understanding the origins of PM and its chemical species in Qassim districts is advantageous for the purpose of identifying impacts on one’s health Characterization of pollution sources also will assist in the success of mitigation strategies and solve the issue of increased PM concentrations in outdoor areas. Therefore, this study described the physical and chemical properties of PM by measuring the PM1, PM2.5, and PM10 concentrations in Qassim’s central urban and rural areas. In urban Qassim, the average PM1, PM2.5, and PM10 concentrations were 12.23, 33.16, and 155.38 µg/m3, respectively. On the other hand, the average PM1, PM2.5, and PM10 concentrations reached 8.18, 23.03, and 93.57 µg/m3 in rural Qassim, respectively. The findings revealed that fine particle concentrations have risen over allowable levels due to dust storms. A total of 18 elements and 8 chemical compounds were determined and identified. The compound concentrations of Al2O3, SiO2, CaO, TiO2, K2O, BaO, MnO, Fe2O3 and the essential elements Sr, V, Al, Cu, Ca, Fe, Cr, As, Zr, K, Br, Ba, Pb, Mn, Ni, Zn, Rb and Ti were determined using XRF and XRD. The findings revealed that fine particle concentrations have risen over allowable levels due to dust storms. The main elements in the trace elements were Ca, Fe, Al, Sr, K, and Mg, indicating significant contributions from the soil (Ca, Fe, Al, Mg), as well as from industrial and vehicle emissions (Sr, Ca, Al, Fe, K). V, Cu, Zn, Cr, Ni, Pb, Sr, As, Mn and Br fall under the anthropogenic category of trace elements, while Al, S, Na, Mg, Rb, K, Zr, Ti, Fe, Mn, Sr, Y, Cr, Ca, Ni, Zn and Cu are obtained from the earth's crust. HIGHLIGHTS The study measured levels of toxic heavy metals in airborne particulate matter (PM) in the Qassim region of Saudi Arabia The results indicated that the concentrations PM were higher than the recommended guidelines set by the World Health Organization (WHO), which poses a potential health risk to the population The study suggests that the major sources of heavy metals in the Qassim region are from dust storms and anthropogenic activities such as industrial activities and traffic emissions The study highlights the importance of regular monitoring of heavy metals in PM and the need for effective pollution control measures to protect public health GRAPHICAL ABSTRACT

We breathe pesticides: the presence of contaminants of the breathable and inhalable fractions of atmospheric particulate matter

Atmospheric air contamination by pesticides present in particulate matter is considered ubiquitous and can be found in different regions of the world. Atmospheric particulate matter can be collected by separating the fine from the coarse particles, simulating the action in the respiratory tract. The presence of pesticides in particulate matter fractionated by size was investigated in a peri-urban region, where agriculture is intense. Weekly samples were obtained using a cascade impactor. The gravimetric determination was obtained using an ultra-analytical balance. Identification and quantification of pesticides were obtained using UHPLC-MS/MS for 105 pesticides. The average concentration of total PM of 25.2 µg m-3 and maximum concentration of 44.2 µg m-3 were obtained. Azoxystrobin, carbendazim, chlorimuron, difenoconazole, diflubenzuron, ethephon, metolachlor, pyraclostrobin and trifloxystrobin were determined in the inhalable fraction (2.5 to 10.0 µm). In the fine fraction (1.0 to 2.5 µm) carbendazim, ethephon, metolachlor and trifloxystrobin were determined and in the 0.5 to 1.0 µm fraction, clomazone, ethoprophos, fenpropimorph, metalaxyl, pyrimethanil and propoxur were determined. Cyproconazole and metolachlor were determined in the <0.25 µm range and acephate was found in the coarse and fine ranges. The population in the region is exposed to possible risks when breathing particulate matter containing pesticides. These studies are essential to support public policies and more sustainable agricultural practices, promoting food security and environmental protection.

Particulate matter pollution and non-targeted analysis of polar compounds in three regions of Brazil

Atmospheric Particulate Matter (PM) is a pollutant with diverse origins, exhibiting varying chemical compositions, and undergoes several molecular transformations in the atmosphere. In this study, PM samples (PM2.5, PM10 and TSP) were collected in five Brazilian cities (Camboriú-SC; Catalão-GO; Florianópolis-SC; Limeira-SP and Novo Hamburgo-RS) during the four seasons of the year. Analysis of Variance (ANOVA) was used to evaluate the differences between each city and season in PM concentration. PM10 average concentrations were higher in the city of Limeira, compared to the other (ANOVA p-values and Tukey's test). Moreover, Tukey's test demonstrated differences between the average PM10 concentrations in summer and winter. Regarding TSP and PM2.5, Tukey's test showed differences between winter and warm seasons (spring and summer). Moreover, polar compounds from the samples collected in the summer (February) and winter (August) periods were analyzed (Ultra-High-Performance Liquid Chromatography coupled to a Quadrupole Time-of-Flight Mass Spectrometer) following a non-targeted approach and annotated. This is the first study to carry out this type of analysis in these five Brazilian cities. Despite the differences in PM concentrations, profiles of polar organic compounds, showed similarities between samples/and, in general, the same compounds were present, albeit with different intensities. The annotated compounds are associated with vehicle emissions and plastics, which are considered important global air polluters. Therefore, there is an urgent necessity for comprehensive studies aimed at investigating the non-targeted compounds existing in the atmosphere. Such research can provide invaluable insights to policymakers, enabling them to formulate effective guidelines and policies to mitigate particulate matter concentration and enhance overall air quality.

Detection of SARS-CoV-2 in the indoor air and surfaces of subway trains in Mashhad, Iran.

Millions of passengers around the world are concerned with the possibility of SARS-CoV-2 contamination on public transportation. Therefore, this study aimed to investigate the presence of SARS-CoV-2 virus in indoor air and subway surfaces in Mashhad. In this study, air and surface sampling were done at two times in the morning (7-8:30 a.m.) and evening (3:30-5 p.m.), simultaneously in two wagons for men and women in line 1 of Mashhad Metro in March 2021 to detect the virus and measure the concentration of particulate matter. Totally, 30 air and 30 metro samples were collected and examined by reverse transcriptase-polymerase chain reaction (RT-PCR). The results showed that three and two cases in the air and surface samples were infected with the SARS-CoV-2 virus, respectively. There was a significant relationship between the mean concentration of suspended particles PM1 (particulate matter smaller than 1 μm) with PM2.5 (particulate matter smaller than 2.5 μm) and PM10 (particulate matter smaller than 10 μm) (p < 0. 05). There was also a significant relationship between the mean concentration of suspended particles PM2.5 and PM10. The results showed that the mean PM2.5 measured in the indoor air of the Mashhad metro wagon had a significant relationship with WHO and US EPA and national standards, and its value was higher than the standards (p < 0.05). The average particle concentrations of PM1, PM2.5, and PM10 were equal to 40.46, 42.61, and 48.31 μg/m3. According to the results of the pollution detected in this study, COVID-19 may be transmitted by air and environmental surfaces. Our study emphasizes the need for continuous assessment of the presence of the virus in public transportation. Detection of viral RNA in subways indicates the necessity of adequate disinfection in public settings, strictness in disinfection methods, strengthening of educational activities for sanitary measures, physical spacing plan, and increasing ventilation of wagons.

Characteristics and atmospheric processes of water-soluble ions in PM2.5 and PM10 over an industrial city in the National Capital Region (NCR) of India

This study investigates water-soluble inorganic ions (WSIIs), their potential formation mechanism, particle bound water content and acidity (pH) in ambient aerosols in Ghaziabad, an industrial city located downwind of Delhi, the Indian National Capital. 24-hour integrated PM2.5 and PM10 samples were collected using collocated samplers at two receptor locations every third day throughout the year. The samples were collected on Nylon filter substrates (preceded by a denuder upstream for capturing acidic gases) and Teflon filter substrates for WSII and mass measurements, respectively. Further, on-site meteorological parameters were recorded at both locations. The annual average PM concentrations observed were: PM2.5: 144 ± 81 μgm−3 (Site A), 163 ± 95 μgm−3 (Site B); and PM10: 245 ± 116 μgm−3 (Site A), 279 ± 152 μgm−3 (Site B). On average, SO42−, NO3−, Cl− and NH4+ contributed to ∼90% to PM2.5WSII mass and ∼82% to PM10WSII mass, which in turn made up ∼35% of PM2.5 mass and ∼23% of the PM10 mass across both sites. SO42− concentration was found to be highest followed by NO3−, NH4+ and Cl− for both PM fractions. Mann-Whitney U test revealed that both sites were similar with respect to WSII mass indicating city's airshed average concentration with respect to the major ions. The highest WSII contribution to the total PM was found in winter season (37–40% for PM2.5 and 27–31% for PM10) followed by monsoon (36–38% for PM2.5 and 18–21% for PM10), post-monsoon (28–34% for PM2.5 and 21–23% for PM10) and pre-monsoon (21–27% for PM2.5 and 15% for PM10). High contribution during winter was because of higher emissions of precursor gases in combination with moderate relative humidity (36–59%) and minimal precipitation (average ∼0.2 mm). Sulphur Oxidation Ratio and Nitrogen Oxidation Ratio indicated high photochemical oxidation with a greater extent of SO42− formation compared to NO3−. Although high concentrations of NH3 gas were observed, there was insufficient NH4+ ions for neutralizing counter ions with Ammonium Neutralization Ratio values of 0.76±0.14 for PM2.5 and 0.62±0.14 for PM10. Finally, aerosol liquid water content (ALWC) and acidity were estimated using a thermodynamic equilibrium model, ISORROPIA-II. It was found that PM2.5 pH varied from 2.9 to 6.2 (avg. 4.4±0.8) and the PM2.5 mass doubled at RH ∼90% because of the high particle bound water. Consequently, a reduction in SO2 would result in instantaneous cuts of SO42−, PM2.5, and ALWC.

Comparison of the Air Quality within the Northern Region of India both During and After the Lockdown

The World Health Organization (WHO) declared COVID-19 a global pandemic due to the novel infectious coronavirus disease found in late 2019. Some positive impacts have been seen on the natural environment during the outbreak of COVID-19. In this study, we have tried to analyse the impact of lockdown on air quality at four major northern states of India (Delhi, Uttar Pradesh, Rajasthan, and Haryana) located in the National Capital Region (NCR). This study compared the variation in air pollutants during the first lockdown phase (25th March to 15th April) 2020 and after the lockdown phase (25th March to 15th April) 2022, including PM10, PM2.5, NO2, and SO2. The average concentration of PM10, PM2.5, NO2, and SO2 reduced by 70.43%, 64.7%, 66.37%, and 36.89% over National Capital Region (NCR) during the lockdown phase. A good pollution control plan can lead to significant improvements in air quality in the future, which should provide confidence to policymakers involved in developing air quality policies.

The association between PM2.5 concentration and the severity of acute asthmatic exacerbation in hospitalized children: A retrospective study in Chongqing, China.

Ambient PM2.5 is associated with asthma exacerbation. The association between the concentration of PM2.5 and the severity of asthma exacerbation has yet to be thoroughly clarified. The study aims to explore the association between the piror 30 days average concentration of PM2.5 and the severity of acute asthma exacerbation in hospitalized children. A total of 269 children with acute exacerbation of asthma were enrolled and divided into three groups according to the PM2.5 exposure concentrations: group 1 (PM2.5: <37.5 μg/m3 ), group 2 (PM2.5: 37.5-75 μg/m3 ), group 3 (PM2.5: ≥75 μg/m3 ), respectively. The ordered logistic regression modeling was conducted to explore the influence of daily PM2.5 concentration on the clinical severity of children's asthma exacerbation. Multiple linear regression was conducted to explore the association between the concentration of PM2.5 and the length of stay in the hospital (LOS). We also conducted a receiver operating characteristic (ROC) curve analysis to explore the cutoff value of PM2.5 to predict the children's asthma exacerbation. There was no statistical difference among the three groups of children in gender, age, body mass index, ethnicity, the first diagnosis of asthma, allergic history, passive smoke exposure, or family history of asthma. There was a statistically significant difference in many hospitalization characteristics (p < 0.05) among the three groups of children. Significant differences were found in terms of accessory muscles of respiration (p = 0.005), respiratory failure (p = 0.012), low respiratory tract infectious (p = 0.020), and the severity of asthma exacerbation (p < 0.001) among the three groups. PM2.5 concentration was primarily positively correlated to neutrophile inflammation. The ordered multivariate logistic regression model showed that higher PM2.5 concentrations were significantly associated with greater odds of more severe asthma exacerbation in one and two-pollutant models. The adjusted odds ratio of severe asthma exacerbation was 1.029 (1.009, 1.049) in the one-pollutant model. The most significant odds ratio of severe asthma exacerbation was 1.050 (1.027, 1.073) when controlling NO2 in the two-pollutant models. Multiple linear regression showed that PM2.5 concentration was significantly associated with longer LOS in both one-pollutant and two-pollutant models. By performing ROC analysis, the average daily concentration of 44.5 µg/m3 of PM2.5 (AUC = 0.622, p = 0.002) provided the best performance to predict severe asthma of children exacerbation with a sensitivity of 59.2% and a specificity of 63.8%. The increased prior 30 days average concentration of PM2.5 was associated with greater asthma exacerbation severity and longer length of stay in the hospital of children with asthma exacerbation.

Seasonal Variation and Source Identification of PM10 in an Industrialized City

AbstractA 1‐year PM10 sampling campaign from January 2019 to December 2019 is conducted in an urban industrial area Raipur, India. For the development of effective pollution control strategies, quantitative assessment of potentially toxic metals and accurate identification of pollutant sources are essential. The annual average PM10 aerosol mass concentration during the study period is recorded as 187.89 ± 91 µg m−3. PM10 concentrations are found to vary with meteorological parameters such as wind speed, temperature, rainfall, and humidity. Heavy metals including Fe, Mn, Ni, Pb, Zn, Al, Ba, Cr, Sr, Ga, Mg, and V are studied using atomic absorption spectroscopy (AAS). Seasonally, average winter time PM10 concentration (234.86 ± 104.3 µg m−3) is accounted higher as compared to summer (169.02 ± 27.1 µg m−3) and rainy season (106.28 ± 32.6 µg m−3) during the study period. The aerosol sources identified by positive matrix factorization (PMF) model are: iron and steel production (34.7%), vehicular emissions (29.3%), road dust (14.6%), heavy oil/petroleum (14.6%), and coal combustion (6.9%). Among these sources, iron and steel production contributes maximum in PM10 concentrations. The probable source locations are recognized through concentration weighted trajectory (CWT) analysis. The AirQ+ model is used to assess the health effects of regional PM10 concentrations.

A Study on the Influence of Air Pollution on Temperature Forecast Skill Based on Operational Weather Forecast in BTH Region

Surface air temperature is a comprehensive function of aerosols in the atmosphere and various weather factors. However, there is no real-time aerosol concentration feedback in most operational numerical weather prediction (NWP) models. This raises a scientific question of how abnormal changes in air pollutants in a short period of time will affect the temperature prediction skill of NWP models. Thus, the study was carried out to investigate the possible influence of air pollution on the temperature forecast skill based on the operational NWP model over the Beijing–Tianjin–Hebei (BTH) region during January–February 2020. The results show that the average concentrations of PM2.5, SO2, NO2 and CO over the BTH region in February were smaller than those in January by 38.5%, 35.1%, 48.0% and 33.1%, respectively. Simultaneously, the forecast skills for surface temperature in February from both regional (RMAPS, Rapid-refresh Multi-scale Analysis and Prediction System) and global (ECMWF, European Centre for Medium-Range Weather Forecasts) operational NWP models improved markedly compared with that in January. In both models, the underestimation of maximum temperature and the overestimation of minimum temperature in most cities over the BTH region in February were significantly reduced. With the 24 h (24 h) forecast lead time, the RMSE (root mean square error) of BTH daily mean, maximum and minimum temperature prediction in February based on RMAPS were 17.3%, 9.8% and 21.6% lower than that in January, respectively. These are generally consistent with the other statistical indices such as deviation and regression coefficient. As the forecast lead time extended to 48 h and 72 h forecast, the phenomena still existed and were also evident in the ECMWF model. The improvement of temperature forecast skill of NWP models may be attributed to the unexpected dramatical reduction of air pollutants. Less aerosols during the daytime allow more solar radiation reaching the surface and cause a warming in the near-surface temperature, while less aerosols during the nighttime favor the outgoing long-wave radiation and then lead to a cooling near the ground.

Clearing the Air: Assessing the Effectiveness of Emission Policy in Qinhuangdao’s Key Industries

China has successively put forward ultra-low emission (ULE) transformation plans to reduce the air pollutant emissions of industrial pollutants since 2014. To assess the benefits of the ULE policy on regional air quality for Qinhuangdao, this study developed an emission inventory of nine atmospheric pollutants in 2016 and evaluated the effectiveness of the emission policy in Qinhuangdao’s key industries under different scenarios with an air quality model (CALPUFF). The emissions of air pollutants in 2016 were as follows: Sulfur dioxide (SO2) emitted 48.91 kt/year, nitrogen oxide (NOx) emitted 86.83 kt/year, volatile organic compounds (VOCs) emitted 52.69 kt/year, particulate matter (PM10 and PM2.5) emitted 302.01 and 116.85 kt/year, carbon monoxide (CO) emitted 1208.80 kt/year, ammonia (NH3) emitted 62.87 kt/year, black carbon (BC) emitted 3.79 kt/year, and organic carbon (OC) emitted 2.72 kt/year, respectively. The results showed that at the regional level in 2025, the iron and steel industry under the PPC (Peak Production Capacity) scenario had the highest potential for reducing SO2 and NOx emissions, while the cement industry under the PPC scenario excelled in reducing PM10 emissions. As for the industrial level in 2025, the flat glass industry under the ULE scenario would reduce the most SO2 emitted, while the iron and steel industry and the cement industry under the PPC scenario demonstrated the best reduction in NOx and PM10 emissions, respectively. Furthermore, the average annual contribution concentration of SO2, NOx, and PM10 in the air monitoring stations of Qinhuangdao under the PPC scenario was significantly lower than that under the BAU scenario revealed by air quality simulation. It can be concluded that the emission policy in Qinhuangdao will help improve the air quality. This study can provide scientific support for policymakers to implement the ULE policy in industrial undeveloped cities and tourist cities such as Qinhuangdao in the future.

Spatiotemporal characteristics and driving mechanisms of PM10 in arid and semi-arid cities of northwest China

PM10 pollution is a pressing ecological concern within arid and semi-arid cities, adversely influencing the natural environment and human health. Based on the Geographically and Temporally Weighted Regression (GTWR) model, this study comparatively investigated the spatiotemporal dynamics of PM10 during non-dust and dust periods in five arid cities (Jiuquan, Jiayuguan, Zhangye, Jinchang, and Wuwei) and one semi-arid city (Lanzhou) in northwest China. Further, this study examined the critical mechanisms of PM10 via the GTWR and Geographic Detector model. A comparative analysis illustrated that dust period PM10 concentrations surpassed non-dust period concentrations. The annual average PM10 concentrations during the non-dust period wavily declined from 2015 to 2020, exhibiting significantly higher levels in the semi-arid city as opposed to arid cities. Conversely, during the dust period, PM10 levels rose initially between 2015 and 2017 and diminished from 2018 to 2020, with high concentration clusterings primarily observed in 2015–2016 in western arid cities, Jiuquan and Jiayuguan, in 2017–2019 in eastern cities, Wuwei and Lanzhou, and in 2020 in central arid cities, Jinchang and Zhangye. During the non-dust period from 2015 to 2020, the cumulative effects of single driving factors were ranked in the following order: soil moisture content (MOI), geographical location (LOC), precipitation (PRE), SO2, gross domestic product (GDP), wind speed (WS), and digital elevation (DEM) while during the dust period, the emphasized factors were ranked in the following order: MOI, LOC, PRE, WS, SO2, GDP, and DEM. The interaction between MOI and WS on PM10 was most noticeable in this study (non-dust: 0.787, dust: 0.832). Thus, MOI and WS are critical components in controlling PM10 pollution. The outcomes of this study can furnish theoretical foundations for ecological construction in Northwest China, unlock novel insights for ecological measures in other global arid and semi-arid cities.