Comparison of air pollutant-related hospitalization burden from AECOPD in Shijiazhuang, China, between heating and non-heating season.

BackgroundPrevious studies have found associations between asthma morbidity and air pollution especially in young population, (PLoS One 12:e0180522, 2017; Can J Public Health 103:4-8, 2012; Environ Health Perspect 118:449-57, 2010; Am J Respir Crit Care Med 182:307-16, 2010; J Allergy Clin Immunol 104:717-22, 2008; J Allergy Clin Immunol 104:717-22, 1999; Environ Res 111:1137-47, 2011) but most of them were conducted in areas with relatively low air pollutant level. Moreover, very few studies have investigated the effect and burden modification of heating season during which the ambient air pollution level is significantly different from that during non-heating season in north China.ObjectivesThis study aimed to evaluate the effect and burden modification of heating on short-term associations between adult asthma hospitalizations and ambient air pollution in the north China city of Shijiazhuang.MethodsGeneralized additive models combined with penalized distributed lag nonlinear models were used to model associations between daily asthma hospitalizations and ambient air pollutants from 1 January 2013 to 16 December 2016 in Shijiazhuang city, adjusting for long-term and seasonality trend, day of week, statutory holiday, daily mean air pressure and temperature. Attributable risks were calculated to evaluate the burden of asthma hospitalizations due to air pollutants exposure. The effect of pollutants on hospitalization and the attributable measures were estimated in heating and non-heating season separately and the comparisons between the two seasons were conducted.ResultsAll pollutants demonstrated positive and significant impacts on asthma hospitalizations both in heating season and non-heating season, except for O3 in heating season where a negative association was observed. However, the differences of the pollutant-specific effects between the two seasons were not significant. SO2 and NO2 exposure were associated with the heaviest burden among all pollutants in heating season; meanwhile, PM10 and PM2.5 were associated with the heaviest burden in heating season.ConclusionsIn conclusion, we found evidence of the effect of ambient air pollutants on asthma hospitalizations in Shijiazhuang. The central heating period could modify the effects in terms of attributable risks. The disease burden modification of heating should be taken into consideration when planning intervention measures to reduce the risk of asthma hospitalization.

In urban North China, nitrate () is a primary contributor to haze formation. So far, the production processes and source apportionments of atmospheric during the heating season (i.e. the wintertime) have not yet been well understood. This study determined δ15N–, δ18O–, and Δ17O– of aerosol samples to compare the potential sources and formation pathways of atmospheric during heating (November to March) and non-heating (April to May) seasons. Combining stable isotope composition with the MixSIAR model based on Δ17O– showed that NO3 + DMS/HC (dimethyl sulfate/hydrocarbon) pathway was the dominant process of atmospheric nitrate formation during the heating season (mean = 52.88 ± 16.11%). During the non-heating season, the contributions of NO3 + DMS/HC (mean = 37.89 ± 13.57%) and N2O5 + H2O (mean = 35.24 ± 3.75%) pathways were comparable. We found that Δ17O– was negatively correlated with wind speed and positively correlated with relative humidity during the heating season, possibly associated with the sources and production of atmospheric . In specific, in a dust storm event, the very low Δ17O– is likely associated with particles from land surface. Under the premise of considering 15N fractionation, the constraint-based on δ15N– illustrated that coal combustion was the major source of NO x emission during the heating season, and the relative contribution of coal combustion decreased rapidly from the heating season (mean = 42.56 ± 15.50%) to the non-heating season (mean = 21.86 ± 4.91%). Conversely, the proportion of NOx emitted by soil microbes rose significantly from the heating (mean = 9.67 ± 5.99%) to non-heating season (mean = 24.02 ± 11.65%). This study revealed differences in the sources and formation processes of atmospheric during the heating and non-heating seasons, which are of significance to atmospheric nitrogen oxide/nitrate pollution mitigation.

Spatial distribution differences in PM2.5 concentration between heating and non-heating seasons in Beijing, China

Background:Air pollutants and their pathogenic effects differ among regions and seasons. We aimed to explore the relationship between fine particulate matter (PM2.5), sulfur dioxide (SO2), and ozone-8 hours (O3-8h) concentrations in heating and non-heating seasons and the associated death risk due to cardiovascular diseases (CDs), respiratory diseases (RDs), and malignant tumors.Methods:Data were collected in Shenyang, China, from April 2013 to March 2016. We analyzed the correlation or lagged effect of atmospheric pollutant concentration, meteorological conditions, and death risk due to disorders of the circulatory system, respiratory system, and malignant tumor in heating and non-heating seasons. We also used multivariate models to analyze the association of air pollutants during holidays with the death risk due to the evaluated diseases while considering the presence or absence of meteorological factors.Results:An increase in the daily average SO2 concentration by 10 μg/m3 increased the death risk by CDs, which reached a maximum of 2.0% (95% confidence interval [CI]: 1.3%–2.7%) on lagging day 4 during the non-heating season and 0.2% (95% CI: 0.1%–0.4%) on lagging day 3 during the heating season. The risk of death caused by RDs peaked on lagging day 1 by 0.8% (95% CI: 0.4%–1.2%) during the heating season. An increase in O3-8h concentration by 10 μg/m3 increased the risk of RD-related death on lagging day 2 by 1.0% (95% CI: 0.4%–1.7%) during the non-heating season, which was significantly higher than the 0.1% (95% CI: 0–0.9%) increase during the heating season. Further, an increase in the daily average PM2.5 concentration by 10 μg/m3 increased the risk of death caused by RDs by 0.3% and 0.8% during heating and non-heating seasons, respectively, which peaked on lagging day 0. However, air pollution was not significantly associated with the risk of death caused by malignant tumors.Conclusion:Short-term exposure to PM2.5, SO2, and O3 during the non-heating season resulted in higher risks of CD-related death, followed by RD-related death.

Polycyclic aromatic hydrocarbon (PAH) emissions from the combustion of household solid coal for cooking and heating cause great harm to public health in China, especially in less developed areas. Children are one of the most susceptible population groups at risk of indoor air pollutants due to their immature respiratory and immune systems. However, information on PAH exposure of children is limited due to limited monitoring data. In this study, we aimed to assess the seasonal differences of PAHs in classrooms, analyze the pollutant sources, and calculate the incremental lifetime cancer risk attributable to PAHs in Shanxi Provence. A typical school using household coal combustion in Shanxi Province was selected. Fine particulate matter (PM2.5)samples were collected by both individual samplers and fixed middle-flow samplers during the heating and non-heating seasons in December 2018 and April 2019. The PAH concentrations in PM2.5 samples were analyzed by a gas chromatograph coupled to a mass spectrometer. The results showed that PAH concentrations in PM2.5 varied between 89.1 ng/m3 in the heating season and 1.75 ng/m3 in the non-heating season. The mean concentrations of benzo[a]pyrene (BaP), a carcinogenic marker of PAHs, were 10.3 and 0.05 ng/m3 in the heating and non-heating seasons, respectively. Source allocation analysis of individual portable and passive samplers revealed that the main contributors during heating and non-heating seasons were coal combustion and gasoline sources, respectively. According to the results of a Monte Carlo simulation, the incremental lifetime cancer risk values from the inhalation of PAHs in the heating and non-heating seasons were 3.1 × 10−6 and 5.7 × 10−8, respectively. The significant increase in PAHs and the incremental lifetime cancer risk in the heating season indicates that children are more exposed to health threats in winter. Further PAH exposure control strategies, including reducing coal usage and promoting clean fuel applications, need to be developed to reduce the risk of PAH-induced cancer.

Raman microspectroscopy and thermo-optical-transmittance (TOT) method were used to study airborne ambient soot collected at the suburban air monitoring station in southern Poland during the residential heating (January-February) and non-heating (June–July) seasons of 2017. Carbonaceous material constituted on average 47.2 wt.% of PM2.5 during the heating season and 26.9 wt.% in the non-heating season. Average concentrations of OC (37.5 ± 11.0 μg/m3) and EC (5.3 ± 1.1 μg/m3) during the heating season were significantly higher than those in the non-heating season (OC = 2.65 ± 0.78 μg/m3, and EC = 0.39 ± 0.18 μg/m3). OC was a chief contributor to the TC mass concentration regardless of the season. All Raman parameters indicated coal combustion and biomass burning were the predominant sources of soot in the heating season. Diesel soot, which is structurally less ordered than soot from other sources, was dominant during the non-heating season. The D1 and G bands area ratio (D1A/GA) was the most sensitive Raman parameter that discriminated between various soot sources, with D1A/GA > 1 for diesel soot, and less than 1 for soot from coal and wood burning. Due to high daily variability of both TOT and Raman spectroscopy data, single-day measurements can be inconclusive regarding the soot source apportionment. Long-time measurement campaigns are recommended.

Black carbon (BC) aerosols have a considerable impact on humans because they not only cause environmental pollution and reduce visibility but also harm human health. During the heating season in northern China, a large amount of coal is burned for heating, producing a large amount of BC. There are few studies on BC properties during the heating season. In this paper, BC is measured optically, so it is referred to as equivalent black carbon (EBC). This paper investigated EBC properties in depth during the heating and nonheating seasons of a typical urban environment in China with two years of EBC measurements. The results show that: (1) EBC aerosol concentrations during the heating season were significantly higher than those during the nonheating season. (2) The main sources of EBC aerosols throughout the year are liquid sources. During the heating season, solid sources (coal and biomass combustion) are dominant. (3) The proportion of brown carbon (BrC) produced by biomass energy during the heating season is greater than that during the nonheating season. (4) The resulting backward trajectory indicates that a large portion of the high EBC aerosol concentration sources originate from northern and northwestern China. Our results reveal that the characteristics and sources of EBC in the urban environment of northern China vary widely, suggesting that different measures should be taken to reduce BC aerosol concentrations during heating and nonheating seasons.

Emission control priority of PM2.5-bound heavy metals in different seasons: A comprehensive analysis from health risk perspective

Coal combustion for winter heating is a major source of heavy atmospheric pollution in China, while its impacts on black carbon (BC) are not yet clear. A dual-spot Aethalometer was selected to monitor the atmospheric BC concentration in Zhengzhou, China, during the heating season, which is from 15 November through 15 March of the following year, and the non-heating season (days other than heating season). The characteristics and sources of BC were analyzed, and a concentration weight trajectory (CWT) analysis was conducted. The results showed that the BC concentrations in the heating season were generally higher than those in the non-heating season. The diurnal variation in BC concentrations during heating season was bimodal, and that during the non-heating season was unimodal. The α-values in the heating and non-heating seasons indicated that combustion of coal and biomass and vehicle emissions were the major BC sources for the heating season and non-heating season, respectively. BC concentrations were positively correlated with PM2.5, PM10, CO, and NOX. There was a strong negative correlation between wind speed and BC concentrations, and that for relative humidity was the opposite. BC concentration during heating season was mainly influenced by the northwestern areas of China and the eastern part of Henan, and that in the non-heating season was mainly from the northeastern areas of China and southern Henan.

(1) The association of the indoor environmental conditions in classrooms with illness-related absenteeism (IRA) was not well investigated. In addition, studying the association between heating and non-heating seasons were very limited; (2) To fill this knowledge gap, a research team collected various indoor air quality (IAQ) and thermal comfort conditions (TC) of 85 elementary classrooms in two school districts from the Midwestern United States throughout an academic year; in total, 255 classroom visits were performed. A negative binomial regression model was implied to associate the classroom’s IAQ and TC with IRA, separating for heating and non-heating seasons; (3) During non-heating season, a 3% increase of IRA was estimated with 1,000,000-counts/L increase of particles that had a diameter less than 2.5 μm (PN2.5); during the heating season, a 3% increase of IRA were expected with 100 ppm increase of room averaged CO2 concentration; and (4) These results suggested that the IAQ and TC factors could associated with IRA differently between heating and non-heating seasons.

Airborne micro- and nanoplastics (MNPs) are emerging pollutants of concern due to their widespread presence, inhalation exposure risks, and potential ecological and human health impacts. In this study, pyrolysis-thermal desorption-gas chromatography-mass spectrometry (Py-TD-GC-MS) was applied to quantify MNPs (PP, PE, PS, PVC, and PET) in PM2.5, PM10, and total suspended particulate matter (TSP) collected during the non-heating and heating seasons in Beijing. The average concentrations of MNPs in PM2.5, PM10, and TSP during the non-heating season were 0.21 ± 0.05 μg/m3, 0.45 ± 0.20 μg/m3, and 0.84 ± 0.42 μg/m3, respectively, rising to 0.41 ± 0.12 μg/m3, 1.23 ± 0.58 μg/m3, and 2.30 ± 0.75 μg/m3 during the heating season, with polyethylene (PE) dominating all size fractions (>50%). Complementary vibrational spectroscopy results confirmed the presence of additional polymers, including polyamide (PA) and poly(methyl methacrylate) (PMMA), highlighting the value of multimethod approaches for comprehensive characterization and the ongoing need to develop new analytical techniques. Exposure assessment indicated daily per capita inhalation of outdoor MNPs in urban Beijing at 60-344 ng during the non-heating season and 374-926 ng during the heating season. This study provides the first size-resolved, multimethod assessment of airborne MNPs in a northern Chinese megacity, with a comparative analysis between heating and non-heating seasons.

A land use regression for predicting NO 2 and PM 10 concentrations in different seasons in Tianjin region, China

The objectives of this study were: to investigate and compare the mass concentration of size-resolvedparticulate matter (PM1, PM2.5, PM4, PM10, and PM35) in indoor air of three rooms of a selected firestation in Poland (i.e. common room, laundry room, and garage); to compare them with the massconcentration of size-resolved PM in a single-family residential building; and to estimate andcompare the health exposure of occupants of these two building types related to measured PMconcentrations. At each point, measurements were conducted for 12 hours a day for 7 days in heating(26/01/2025–24/02/2025) and non-heating (27/05/2025–27/06/2025) seasons using Grimm 11-Daerosol spectrometer (optical method). As indicated by the data, elevated concentrations of PM wereobserved in both the fire station and the single-family house during the heating season whencompared to the non-heating season. During the heating season, the mean PM concentrationsranged from 17.1 to 68.0 μg/m3 and 17.5 to 48.4 μg/m3, while during the non-heating season, theyranged from 6.8 to 42.4 μg/m3 and 9.2 to 35.5 μg/m3 for the fire station and single-family house,respectively. At each measurement point, with the exception of the laundry room during the heatingseason, the majority of the PM mass was accumulated as coarse particles (55% to 72%). The exposureassessment demonstrated that the highest values of PM deposition in the lung alveoli were recordedfor the laundry room, garage, and single-family house during the heating season and for the laundryroom during the non-heating season. The results obtained in this study can be used in future studiesto assess the health risks of firefighters exposed to air quality inside fire stations. They can also beused to designate directions for further research in this area.

Complying with strict PM10 and PM2.5 limit values poses challenges in many European regions, influenced by diverse factors such as natural, regional, and local anthropogenic sources. Urban air pollution, exacerbated by road transport, local industry, and dust resuspension, contrasts with rural areas affected by solid fuel-based local heating and increasing wood burning. This study focuses on village of Sučany, located in Slovakia, analysing PM concentrations during non-heating and heating seasons. The method of analysis relies on the use of the MP101M air quality analyser that utilises beta radiation absorption method. One set of measurements was conducted at five distinct locations during the heating season (18/01/2019 to 28/02/2019) and non-heating season (14/08/2018 to 1/10/2018). Significant differences emerged during the non-heating season with corresponding PM10 averages of 23.0 µg/m3 and PM2.5 at 19.3 µg/m3. In contrast, the PM10 averaged 53.9 µg/m3 and 52.8 µg/m3 during the heating season. The heating season shows PM2.5 contributing up to 98% of total PM10. The distribution of PM10 and PM2.5 pollution and the location of the potential source obtained using polar plots differed during the heating and non-heating seasons. This research underscores the impact of local heating on air quality in a typical Slovak village. The key recommendation for targeted interventions is supporting up-to-date air quality data, education, and financial incentives for citizens in order to implement cleaner and modern heating solutions.

Local variation of PM2.5 and NO2 concentrations within metropolitan Beijing