Is PM1 similar to PM2.5? A new insight into the association of PM1 and PM2.5 with children's lung function

Exposures to particulate matters and childhood sleep disorders-A large study in three provinces in China

OBJECTIVES

Evidence on the link between long-term ambient particulate matter (PM) exposures and childhood sleep disorders were scarce. We examined the associations between long-term exposures to PM2.5 and PM1 (PM with an aerodynamic equivalent diameter <2.5 μm and <1 μm, respectively) with sleep disorders in children.

METHODS

We performed a population-based cross-sectional survey in 177,263 children aged 6 to 18 years in 14 Chinese cities during 2012-2018. A satellite-based spatiotemporal model was employed to estimate four-year annual average PM2.5 and PM1 exposures at residential and school addresses. Parents or guardians completed a checklist using the Sleep Disturbance Scale for Children. We estimated the associations using generalized linear mixed models with adjustment for characteristics of children, parents, and indoor environments.

RESULTS

Long-term PM2.5 and PM1 exposures were positively associated with odds of sleep disorders for almost all domains. For example, increments in PM2.5 and PM1 per 10 μg/m3 were associated with odds ratios of global sleep disorder of 1.24 (95 % confidence interval [CI]: 1.14, 1.35) and 1.31 (95 %CI: 1.18, 1.46), respectively. Similar results were observed for subtypes of sleep disorder. These associations were heterogeneous regionally, with stronger associations among children residing in southeast region than in northeast and northwest regions. Moreover, larger estimates of PM1 were found than that of PM2.5 in southeast region.

CONCLUSION

Long-term PM2.5 and PM1 exposures are independently associated with higher risks of childhood sleep disorders, and these associations vary by geographical region.

Association between ambient PM1 and the prevalence of chronic kidney disease in China: A nationwide study

Particulate of diameter ≤ 1 µm (PM1) presents a novel risk factor of adverse health effects. Nevertheless, the association of PM1 with the risk of chronic kidney disease (CKD) in the general population is not well understood, particularly in regions with high PM1 levels like China. Based on a nationwide representative survey involving 47,204 adults and multi-source ambient air pollution inversion data, the present study evaluated the association of PM1 with CKD prevalence in China. The two-year average PM1, particulate of diameter ≤ 2.5 µm (PM2.5), and PM1-2.5 values were accessed using a satellite-based random forest approach. CKD was defined as estimated glomerular filtration rate < 60 ml/min/1.73 m2 or albuminuria. The results suggested that a 10 μg/m3 rise in PM1 was related to a higher CKD risk (odds ratio [OR], 1.13; 95% confidence interval [CI] 1.08-1.18) and albuminuria (OR, 1.11; 95% CI, 1.05-1.17). The association between PM1 and CKD was more evident among urban populations, older adults, and those without comorbidities such as diabetes or hypertension. Every 1% increase in the PM1/PM2.5 ratio was related to the prevalence of CKD (OR, 1.03; 95% CI, 1.03-1.04), but no significant relationship was found for PM1-2.5. In conclusion, the present study demonstrated long-term exposure to PM1 was associated with an increased risk of CKD in the general population and PM1 might play a leading role in the observed relationship of PM2.5 with the risk of CKD. These findings provide crucial evidence for developing air pollution control strategies to reduce the burden of CKD.

Unveiling causal connections: Long-term particulate matter exposure and type 2 diabetes mellitus mortality in Southern China

Evidence of the potential causal links between long-term exposure to particulate matters (PM, i.e., PM1, PM2.5, and PM1-2.5) and T2DM mortality based on large cohorts is limited. In contrast, the existing evidence usually suffers from inherent bias with the traditional association assessment. A prospective cohort of 580,757 participants in the southern region of China were recruited during 2009 and 2015 and followed up through December 2020. PM exposure at each residential address was estimated by linking to the well-established high-resolution simulation dataset. Hazard ratios (HRs) were calculated using time-varying marginal structural Cox models, an established causal inference approach, after adjusting for potential confounders. During follow-up, a total of 717 subjects died from T2DM. For every 1 μg/m3 increase in PM2.5, the adjusted HRs and 95% confidence interval (CI) for T2DM mortality was 1.036 (1.019-1.053). Similarly, for every 1 μg/m3 increase in PM1 and PM1-2.5, the adjusted HRs and 95% CIs were 1.032 (1.003-1.062) and 1.085 (1.054-1.116), respectively. Additionally, we observed a generally more pronounced impact among individuals with lower levels of education or lower residential greenness which as measured by the Normalized Difference Vegetation Index (NDVI). We identified substantial interactions between NDVI and PM1 (P-interaction = 0.003), NDVI and PM2.5 (P-interaction = 0.019), as well as education levels and PM1 (P-interaction = 0.049). The study emphasizes the need to consider environmental and socio-economic factors in strategies to reduce T2DM mortality. We found that PM1, PM2.5, and PM1-2.5 heighten the peril of T2DM mortality, with education and green space exposure roles in modifying it.

Mitigation strategies to reduce particulate matter concentrations in civil engineering laboratories

In the departments of civil engineering, many experiments are conducted in laboratories for educational and research purposes. Varying degrees of respirable dust are generated as the outcome of these experiments, which could cause harm to instructors' and students' health. This study is devised to highlight the importance of indoor air quality in university laboratories. As part of the research, four different particulate matter (PM) sizes (PM1.0, PM2.5, PM4.0, and PM10) were measured during specific experiments-sieve analysis, preparation of the concrete mixture, crushing aggregate by jaw crusher, dynamic triaxial compression test, sieve analysis of silt specimen, cleaning sieve by an air compressor, and proctor compaction test-being conducted periodically in the laboratories of civil engineering departments. The measured values are mainly high compared to indoor air quality standards. Mitigation strategies were applied to reduce indoor air PM levels in the three experiments that contained the highest PM levels. The results have shown that mitigation strategies applied as control measures could make a remarkable difference in protecting instructors and civil engineering students.

Five Years of Accurate PM2.5 Measurements Demonstrate the Value of Low-Cost PurpleAir Monitors in Areas Affected by Woodsmoke

Low-cost optical sensors are used in many countries to monitor fine particulate (PM2.5) air pollution, especially in cities and towns with large spatial and temporal variation due to woodsmoke pollution. Previous peer-reviewed research derived calibration equations for PurpleAir (PA) sensors by co-locating PA units at a government regulatory air pollution monitoring site in Armidale, NSW, Australia, a town where woodsmoke is the main source of PM2.5 pollution. The calibrations enabled the PA sensors to provide accurate estimates of PM2.5 that were almost identical to those from the NSW Government reference equipment and allowed the high levels of wintertime PM2.5 pollution and the substantial spatial and temporal variation from wood heaters to be quantified, as well as the estimated costs of premature mortality exceeding $10,000 per wood heater per year. This follow-up study evaluates eight PA sensors co-located at the same government site to check their accuracy over the following four years, using either the original calibrations, the default woodsmoke equation on the PA website for uncalibrated sensors, or the ALT-34 conversion equation (see text). Minimal calibration drift was observed, with year-round correlations, r = 0.98 ± 0.01, and root mean square error (RMSE) = 2.0 μg/m3 for daily average PA PM2.5 vs. reference equipment. The utitilty of the PA sensors without prior calibration at locations affected by woodsmoke was also demonstrated by the year-round correlations of 0.94 and low RMSE between PA (woodsmoke and ALT-34 conversions) and reference PM2.5 at the NSW Government monitoring sites in Orange and Gunnedah. To ensure the reliability of the PA data, basic quality checks are recommended, including the agreement of the two laser sensors in each PA unit and removing any transient spikes affecting only one sensor. In Armidale, from 2019 to 2022, the continuing high spatial variation in the PM2.5 levels observed during the colder months was many times higher than any discrepancies between the PA and reference measurements. Particularly unhealthy PM2.5 levels were noted in southern and eastern central Armidale. The measurements inside two older weatherboard houses in Armidale showed that high outdoor pollution resulted in high pollution inside the houses within 1-2 h. Daily average PM2.5 concentrations available on the PA website allow air pollution at different sites across regions (and countries) to be compared. Such comparisons revealed major elevations in PA PM2.5 at Gunnedah, Orange, Monash (Australian Capital Territory), and Christchurch (New Zealand) during the wood heating season. The data for Gunnedah and Muswellbrook suggest a slight underestimation of PM2.5 at other times of the year when there are proportionately more dust and other larger particles. A network of appropriately calibrated PA sensors can provide valuable information on the spatial and temporal variation in the air pollution that can be used to identify pollution hotspots, improve estimates of population exposure and health costs, and inform public policy.

The mediating role of exhaled breath condensate metabolites in the effect of particulate matter on pulmonary function in schoolchildren: A crossover intervention study

The role played by metabolites in exhaled breath condensate (EBC) in the effect of PM on schoolchildren's pulmonary function has received little attention. Accordingly, we examined whether metabolites in EBC mediated the effect of PM10, PM2.5, and PM1 on the pulmonary function of schoolchildren at a residential primary school who had received an air-cleaner cross-over intervention. Samples of EBC were collected from a total of 60 schoolchildren and subjected to metabolomics analysis. We found that the effect of PM on six pulmonary function indicators was mediated by the following nine lipid peroxidation-related and energy metabolism-related metabolites present in EBC: 4-hydroxynonenal, arachidoyl ethanolamide, dl-pyroglutamic acid, 5-deoxy-d-glucose, myristic acid, lauric acid, linoleic acid, l-proline, and palmitic acid. However, while all nine of these metabolites mediated the effects of PM on boys' pulmonary function, only 4-hydroxynonenal, arachidoyl ethanolamide, and dl-pyroglutamic acid mediated the effects of PM on girls' pulmonary function. Overall, our results show that (1) short-term exposure to PM affected the schoolchildren's pulmonary function by causing an imbalance between lipid peroxidation and glutathione-based antioxidant activity and by perturbing energy metabolism in respiratory system and (2) there was a sex-dependent antioxidant response to PM exposure, with boys being less resistant than girls.

Short-term exposure to reduced specific-size ambient particulate matter increase the risk of cause-specific cardiovascular disease: A national-wide evidence from hospital admissions

Evidence for the health effects of ambient PM1 (particulate matter with an aerodynamic diameter ≤ 1 µm) pollution is limited, and it remains unclear whether a smaller particulate matter has a greater impact on human health. We conducted a time-series study in 184 major cities by extracting daily hospital data on admissions for ischemic heart disease, heart failure, heart rhythm disturbances, and stroke between 2014 and 2017 from a medical insurance claims database of 0.28 billion beneficiaries. City-specific associations were estimated with over-dispersed generalized additive models. A random-effects meta-analysis was used to estimate regional and national average associations. We conducted stratified and meta-regression analyses to explore potential effect modifiers of the association. We recorded 8.83 million cardiovascular admissions during the study period. At the national-average level, a 10-μg/m3 increase in same-day PM1, PM2.5(particulate matter with an aerodynamic diameter ≤ 2.5 µm) and PM10(particulate matter with an aerodynamic diameter ≤ 10 µm) concentrations corresponded to a 1.14% (95% confidence interval 0.88-1.41%), 0.55% (0.40-0.70%), and 0.45% (0.36-0.55%) increase in cardiovascular admissions, respectively. PM1 exposure was also positively associated with all cardiovascular disease subtypes, including ischemic heart disease (1.28% change; 0.99-1.56%), heart failure (1.30% change; 0.70-1.91%), heart rhythm disturbances (1.11% change; 0.65-1.58%), and ischemic stroke (1.29% change; 0.88-1.71%). The associations between PM1 and cardiovascular admissions were stronger in cities with lower PM1 levels, higher air temperatures and relative humidity, as well as in subgroups with elder age (all P < 0.05). This study provides robust evidence of short-term associations between PM1 concentrations and increased hospital admissions for all major cardiovascular diseases in China. Our findings suggest a greater short-term impact on cardiovascular risk from PM1 in comparison to PM2.5 and PM10.

Gaps and future directions in research on health effects of air pollution

Despite progress in many countries, air pollution, and especially fine particulate matter air pollution (PM2.5) remains a global health threat: over 6 million premature cardiovascular and respiratory deaths/yr. have been attributed to household and outdoor air pollution. In this viewpoint, we identify present gaps in air pollution monitoring and regulation, and how they could be strengthened in future mitigation policies to more optimally reduce health impacts. We conclude that there is a need to move beyond simply regulating PM2.5 particulate matter mass concentrations at central site stations. A greater emphasis is needed on: new portable and affordable technologies to measure personal exposures to particle mass; the consideration of a submicron (PM1) mass air quality standard; and further evaluations of effects by particle composition and source. We emphasize the need to enable further studies on exposure-health relationships in underserved populations that are disproportionately impacted by air pollution, but not sufficiently represented in current studies.

Mechanistic investigation of the influence of defects on armchair unburned carbon for PbCl2 adsorption

As the largest consumer of coal energy, coal-fired power plants emit large amounts of PbCl2 each year, which is of wide concern due to its high toxicity, global migration, and accumulation. Unburned carbon is considered a promising adsorbent for effective PbCl2 removal. However, there is a problem that the current unburned carbon model cannot show the structure of carbon defects on the actual unburned carbon surface. Therefore, it is important to construct defective unburned carbon models with practical significance. In addition, the adsorption mechanism of PbCl2 by an unburned model is not studied deeply enough and the reaction mechanism is not clear yet. This has seriously affected the development of effective adsorbents. To reveal the adsorption mechanism of PbCl2 on unburned carbon, the adsorption mechanism of PbCl2 on defective unburned carbon surfaces was analyzed by using the density flooding theory to investigate the adsorption process of PbCl2 on different unburned carbon models. This will provide theoretical guidance for the design and development of adsorbents for the removal of PbCl2 from coal-fired power plants.

Effect of Acute PM2.5 Exposure on Lung Function in Children: A Systematic Review and Meta-Analysis

Objective

The objective of this study was to conduct a systematic review and meta-analysis to identify the adverse effects of acute PM2.5 exposure on lung function in children.

Design

Systematic review and meta-analysis. Setting, participants and measures: Eligible studies analyzing PM2.5 level and lung function in children were screened out. Effect estimates of PM2.5 measurements were quantified using random effect models. Heterogeneity was investigated with Q-test and I² statistics. We also conducted meta-regression and sensitivity analysis to explore the sources of heterogeneity, such as different countries and asthmatic status. Subgroup analyses were conducted to determine the effects of acute PM2.5 exposure on children of different asthmatic status and in different countries.

Results

A total of 11 studies with 4314 participants from Brazil, China and Japan were included finally. A 10 μg/m³ increase of PM2.5 was associated with a 1.74L/min (95% CI: -2.68, -0.90) decrease in peak expiratory flow (PEF). Since the asthmatic status and country could partly explain the heterogeneity, we conducted the subgroup analysis. Children with severe asthma were more susceptible to PM2.5 exposure (-3.11 L/min per 10 μg/m³ increase, 95% CI -4.54, -1.67) than healthy children (-1.61 L/min per 10 μg/m³ increase, 95% CI -2.34, -0.91). In the children of China, PEF decreased by 1.54 L/min (95% CI -2.33, -0.75) with a 10 μg/m³ increase in PM2.5 exposure. In the children of Japan, PEF decreased by 2.65 L/min (95% CI -3.82, -1.48) with a 10 μg/m³ increase of PM2.5 exposure. In contrast, no statistic association was found between every 10 μg/m³ increase of PM2.5 and lung function in children of Brazil (-0.38 L/min, 95% CI -0.91, 0.15).

Conclusion

Our results demonstrated that the acute PM2.5 exposure exerted adverse impacts on children's lung function, and children with severe asthma were more susceptible to the increase of PM2.5 exposure. The impacts of acute PM2.5 exposure varied across different countries.

Experimental and numerical analysis of indoor air quality affected by outdoor air particulate levels (PM1.0, PM2.5 and PM10), room infiltration rate, and occupants' behaviour

This study conducted an experimental analysis of how indoor air quality (IAQ) is influenced by the outdoor air pollutants levels, infiltration rate, and occupants' behaviours. The impacts of these factors on IAQ were analyzed using on-site measurements and numerical simulations. The results contribute to a better understanding of how to control the Indoor Particulate Level (IPL) for the specific conditions of the studied building. Results showed that occupant behaviour was the primary factor in determining the IPL, significantly changing the number of outdoor particles introduced to the building. Moreover, it was found that the IPL was exponentially correlated to the Outdoor Particulate Level (OPL). Based on numerical simulations, this study concluded that smaller particles do not always have more chance than larger particles of accessing the indoor environment through the building envelope. Meanwhile, a steady-state indoor particle concentration numerical model was established and verified using the 4-fold cross-validation method. Finally, simulation results identified that the room infiltration rate had a positive linear impact on IAQ if the OPL was under 30 μg/m³. This is because the increased air exchange rate can help to dilute indoor air pollutants when the outdoor air is relatively clean.

Association between PM1 Exposure and Lung Function in Children and Adolescents: A Systematic Review and Meta-Analysis

The detrimental effects of PM_2.5 and PM₁₀ (particulate matter less than 2.5 or 10 μm) on human respiratory system, including lung function, have been widely assessed. However, the associations between PM₁ (particulate matter of less than 1 μm) and lung function in children and adolescents are less explored, and current evidence is inconsistent. We conducted a meta-analysis of the literature on the association between PM₁ and lung function in children and adolescents to fill this gap. With no date or language constraints, we used a combination of MeSH (Medical Subject Headings) terms and free text to search PubMed, EMBASE and Web of Science databases through, 1 October 2022 for "PM₁ exposure" and "lung function". A total of 6420 relevant studies were identified through our initial search, and seven studies were included in our study. In this meta-analysis, the fixed effect and random effects statistical models were used to estimate the synthesized effects of the seven included studies. For every 10 μg/m³ increase in short-term PM₁ exposure, forced vital capacity (FVC), forced expiratory volume in the first second (FEV₁), peak expiratory flow (PEF) and maximal mid-expiratory flow (MMEF) decreased by 31.82 mL (95% CI: 20.18, 43.45), 32.28 mL (95% CI: 16.73, 48.91), 36.85 mL/s (95% CI: 15.33, 58.38) and 34.51 mL/s (95% CI: 19.61, 49.41), respectively. For each 10 μg/m³ increase in long-term PM₁ exposure, FVC, FEV₁, PEF and MMEF decreased by 102.34 mL (95% CI: 49.30, 155.38), 75.17 mL (95% CI: 39.61, 110.73), 119.01 mL/s (95% CI: 72.14, 165.88) and 44.94 mL/s (95% CI: 4.70, 85.18), respectively. Our study provides further scientific evidence for the harmful effects of PM₁ exposure on lung function in children and adolescents, indicating that exposure to PM₁ is detrimental to pulmonary health. To reduce the adverse health effects of air pollution on children and adolescents, effective preventive measures should be taken.

Association between short-term exposure to ambient PM1 and PM2.5 and forced vital capacity in Chinese children and adolescents

This study aims to examine the association between short-term exposure to ambient PM₁, PM_1-2.5, and PM_2.5 and forced vital capacity (FVC). Population data were obtained from a school-based cross-sectional survey in Shandong in 2014. Distributed lag non-linear models were used to examine the association between exposure to PM₁, PM_1-2.5, and PM_2.5 and FVC at the day of FVC measurement and the previous 6 days (lag 0 to 6 days). A total of 35,334 students aged 9 to 18 years were included in the study, and the mean exposure concentrations of ambient PM₁, PM_1-2.5, and PM_2.5 for them were 47.4 (standard deviation [SD] = 21.3) μg/m³, 32.8 (SD = 32.2) μg/m³, and 80.1 (SD = 47.7) μg/m³, respectively. An inter-quartile range (IQR, 24 μg/m³) increment in exposure to PM₁ was significantly associated with a lower FVC at lag 0 and lag 1 day (β =  - 80 mL, 95% CI =  - 119, - 42, and β =  - 37 mL, 95% CI =  - 59, - 16, respectively), and an IQR (54 μg/m³) increment in exposure to PM_2.5 was significantly associated with a lower FVC at lag 0 and lag 1 day (β =  - 57 mL, 95% CI =  - 89, - 18, and β =  - 34 mL, 95% CI =  - 56, - 12, respectively) after adjustment for gender, age, body mass index category, residence, month of the survey, intake of eggs, intake of milk, physical activity, and screen time. No significant associations were observed for PM_1-2.5. The inverse associations of PM₁ and PM_2.5 with FVC were larger in males, younger children, those overweight or obese, and those with insufficient physical activity levels. Short-term exposure to ambient PM₁ and PM_2.5 was associated with decreased FVC, and PM₁ may be the primary fraction of PM_2.5 causing the adverse pulmonary effects. Our findings emphasize the need to address ambient PM, especially PM₁, pollution for affecting pulmonary health in children and adolescents.

Characteristics of inhalable bioaerosols on foggy and hazy days and their deposition in the human respiratory tract

Atmospheric bioaerosols contain live and dead biological components that can enter the human respiratory tract (HRT) and affect human health. Here, the total microorganisms in a coastal megacity, Qingdao, were characterized on the basis of long-term observations from October 2013 to January 2021. Particular attention was given to the size dependence of inhalable bioaerosols in concentration and respiratory deposition in different populations on foggy and hazy days. Bioaerosol samples stained with 4,6-diamidino-2-phenylindole (DAPI) were selected to measure the total airborne microbe (TAM) concentrations with an epifluorescence microscope, while a multiple-path particle dosimetry model was employed to calculate respiratory deposition. The mean TAM concentrations in the particle size range of 0.65-1.1 μm (TAM_0.65-1.1) were 1.23, 2.02, 1.60 and 2.33 times those on sunny reference days relative to the corresponding values on days with slight, mild, moderate and severe levels of haze, respectively. The mean concentration of TAMs in the particle size range of 0.65-2.1 μm (TAM_0.65-2.1) on severely hazy days was (2.02 ± 3.28) × 10⁵ cells/m³, with a reduction of 4.16% relative to that on the reference days. The mean TAM_0.65-2.1 concentration changed from (1.50 ± 1.37) × 10⁵ cells/m³ to (1.76 ± 1.36) × 10⁵ cells/m³, with TAM_0.65-1.1 increasing from (7.91 ± 7.97) × 10⁴ cells/m³ to (1.76 ± 1.33) × 10⁵ cells/m³ on days with light fog days and medium fog, respectively. The modeling results showed that the majority of TAM_0.65-2.1 deposition occurred in the extrathoracic (ET) region, followed by the alveolar (AL) region. When different populations were examined separately, the deposition doses (DDs) in adult females and in children ranked at the minimum value (6.19 × 10³ cells/h) and maximum value (1.08 × 10⁴ cells/h), respectively. However, the inhalation risks on polluted days, such as hazy, foggy and mixed hazy-foggy (HF) days, were still below the threshold for adverse impacts on human health.

Long-term PM0.1 exposure and human blood lipid metabolism: New insight from the 33-community study in China

Ambient particles with aerodynamic diameter <0.1 μm (PM_0.1) have been suggested to have significant health impact. However, studies on the association between long-term PM_0.1 exposure and human blood lipid metabolism are still limited. This study was aimed to evaluate such association based on multiple lipid biomarkers and dyslipidemia indicators. We matched the 2006-2009 average PM_0.1 concentration simulated using the neural-network model following the WRF-Chem model with the clinical and questionnaire data of 15,477 adults randomly recruited from 33 communities in Northeast China in 2009. After controlling for social demographic and behavior confounders, we assessed the association of PM_0.1 concentration with multiple lipid biomarkers and dyslipidemia indicators using generalized linear mixed-effect models. Effect modification by various social demographic and behavior factors was examined. We found that each interquartile range increase in PM_0.1 concentration was associated with a 5.75 (95% Confidence interval, 3.24-8.25) mg/dl and a 6.05 (2.85-9.25) mg/dl increase in the serum level of total cholesterol and LDL-C, respectively. This increment was also associated with an odds ratio of 1.25 (1.10-1.42) for overall dyslipidemias, 1.41 (1.16, 1.73) for hypercholesterolemia, and 1.90 (1.39, 2.61) for hyperbetalipoproteinemia. Additionally, we found generally greater effect estimates among the younger participants and those with lower income or with certain behaviors such as high-fat diet. The deleterious effect of long-term PM_0.1 exposure on lipid metabolism may make it an important toxic chemical to be targeted by future preventive strategies.

Enhancing PM2.5 Prediction Using NARX-Based Combined CNN and LSTM Hybrid Model

In a world where humanity's interests come first, the environment is flooded with pollutants produced by humans' urgent need for expansion. Air pollution and climate change are side effects of humans' inconsiderate intervention. Particulate matter of 2.5 µm diameter (PM_2.5) infiltrates lungs and hearts, causing many respiratory system diseases. Innovation in air pollution prediction is a must to protect the environment and its habitants, including those of humans. For that purpose, an enhanced method for PM_2.5 prediction within the next hour is introduced in this research work using nonlinear autoregression with exogenous input (NARX) model hosting a convolutional neural network (CNN) followed by long short-term memory (LSTM) neural networks. The proposed enhancement was evaluated by several metrics such as index of agreement (IA) and normalized root mean square error (NRMSE). The results indicated that the CNN-LSTM/NARX hybrid model has the lowest NRMSE and the best IA, surpassing the state-of-the-art proposed hybrid deep-learning algorithms.

Machine Learning and Meteorological Normalization for Assessment of Particulate Matter Changes during the COVID-19 Lockdown in Zagreb, Croatia

In this paper, the authors investigated changes in mass concentrations of particulate matter (PM) during the Coronavirus Disease of 2019 (COVID-19) lockdown. Daily samples of PM1, PM2.5 and PM10 fractions were measured at an urban background sampling site in Zagreb, Croatia from 2009 to late 2020. For the purpose of meteorological normalization, the mass concentrations were fed alongside meteorological and temporal data to Random Forest (RF) and LightGBM (LGB) models tuned by Bayesian optimization. The models’ predictions were subsequently de-weathered by meteorological normalization using repeated random resampling of all predictive variables except the trend variable. Three pollution periods in 2020 were examined in detail: January and February, as pre-lockdown, the month of April as the lockdown period, as well as June and July as the “new normal”. An evaluation using normalized mass concentrations of particulate matter and Analysis of variance (ANOVA) was conducted. The results showed that no significant differences were observed for PM1, PM2.5 and PM10 in April 2020—compared to the same period in 2018 and 2019. No significant changes were observed for the “new normal” as well. The results thus indicate that a reduction in mobility during COVID-19 lockdown in Zagreb, Croatia, did not significantly affect particulate matter concentration in the long-term..

Simulating PM2.5 Concentrations during New Year in Cuenca, Ecuador: Effects of Advancing the Time of Burning Activities

Fine particulate matter (PM_2.5) is dangerous to human health. At midnight on 31 December, in Ecuadorian cities, people burn puppets and fireworks, emitting high amounts of PM_2.5. On 1 January 2022, concentrations between 27.3 and 40.6 µg m⁻³ (maximum mean over 24 h) were measured in Cuenca, an Andean city located in southern Ecuador; these are higher than 15 µg m⁻³, the current World Health Organization guideline. We estimated the corresponding PM_2.5 emissions and used them as an input to the Weather Research and Forecasting with Chemistry (WRF-Chem 3.2) model to simulate the change in PM_2.5 concentrations, assuming these emissions started at 18:00 LT or 21:00 LT on 31 December 2021. On average, PM_2.5 concentrations decreased by 51.4% and 33.2%. Similar modeling exercises were completed for 2016 to 2021, providing mean decreases between 21.4% and 61.0% if emissions started at 18:00 LT. Lower mean reductions, between 2.3% and 40.7%, or even local increases, were computed for emissions beginning at 21:00 LT. Reductions occurred through better atmospheric conditions to disperse PM_2.5 compared to midnight. Advancing the burning time can help reduce the health effects of PM_2.5 emissions on 31 December.

PM Dimensional Characterization in an Urban Mediterranean Area: Case Studies on the Separation between Fine and Coarse Atmospheric Aerosol

Fine particulate matter (PM) is object of particular attention due to its health effects. It is currently regulated by adopting PM2.5 as an indicator to control anthropogenic combustion emissions. Therefore, it is crucial to collect aerosol samples representative of such sources, without including PM from natural sources. Thus, a clean separation between coarse and fine mode aerosol should be set. With this purpose, aerosol size mass distribution was taken in the aerodynamic diameter range from 0.5 to 10 µm. In comparison with a base scenario, characterized by local pollution sources, three case studies were considered, involving desert dust advection, sea salt advection and forest fire aerosol from a remote area. In the base scenario, PM2.5 represented a suitable fine-mode indicator, whereas it was considerably affected by coarse PM in case of desert dust and sea salt aerosol advection. Such interference was considerably reduced by setting the fine/coarse separation at 1.0 µm. Such separation underrepresented fine PM from forest fire long-range transport, nonetheless in the case studies considered, PM1 represented the best indicator of fine aerosol since less affected by coarse natural sources. The data presented clearly support the results from other studies associating the health effects of PM2.5 to PM1, rather than to PM1–2.5. Overall, there is a need to reconsider PM2.5 as an indicator of fine atmospheric aerosol.

Association between eye-level greenness and lung function in urban Chinese children

BACKGROUND

Health effects of greenness perceived by residents at eye level has received increasing attention. However, the associations between eye-level greenness and respiratory health are unknown. The aim of the study was to investigate the associations between exposure to eye-level greenness and lung function in children.

METHODS

From 2012 to 2013, a total of 6740 school children in seven cities in northeast China were recruited into this cross-sectional study. Forced expiratory volume in 1 s (FEV₁), forced vital capacity (FVC), peak expiratory flow rate (PEF), and maximum mid expiratory flow rate (MMEF) were measured to evaluate lung function and to define lung impairment. Eye-level greenness was extracted from segmented Tencent Map street view images, and a corresponding green view index (GVI) was calculated. Higher GVIs mean more greenness coverage. Mixed-effects logistic regressions were used to estimate the health effects on lung impairment per interquartile range (IQR) increase in GVI. Linear regressions were used to estimate the associations between GVI and lung function. The health effects of ambient air pollutants were also assessed, including particulate matter with an aerodynamic diameter <1.0 μm (PM₁), <2.5 μm (PM_2.5), <10 μm (PM₁₀) as well as nitrogen dioxide (NO₂).

RESULTS

An increase of GVI_800m was associated with lung impairment in FEV₁, FVC, PEF and MMEF, with ORs ranging from 0.68 (95% CI: 0.59, 0.79) to 0.83 (95% CI: 0.74, 0.93). The associations between an IQR increase of GVI_800m and FEV₁ (48.15 ml, 95% CI: 30.33-65.97 ml), FVC (50.57 ml, 95% CI: 30.65-70.48 ml), PEF (149.59 ml/s, 95% CI: 109.79-189.38 ml/s), and MMEF (61.18 ml/s, 95% CI: 31.07-91.29 ml/s) were significant, and PM₁, PM_2.5, and PM₁₀ were found to be mediators of this relationship.

CONCLUSION

More eye-level greenness was associated with better lung function and reduced impairment. However, eye-level greenness associations with lung function became non-significant once lower particulate matter air pollution exposures were considered.

Mapping Total Exceedance PM2.5 Exposure Risk by Coupling Social Media Data and Population Modeling Data

The PM2.5 exposure risk assessment is the foundation to reduce its adverse effects. Population survey-related data have been deficient in high spatiotemporal detailed descriptions. Social media data can quantify the PM2.5 exposure risk at high spatiotemporal resolutions. However, due to the no-sample characteristics of social media data, PM2.5 exposure risk for older adults is absent. We proposed combining social media data and population survey-derived data to map the total PM2.5 exposure risk. Hourly exceedance PM2.5 exposure risk indicators based on population modeling (HEPEpmd) and social media data (HEPEsm) were developed. Daily accumulative HEPEsm and HEPEpsd ranged from 0 to 0.009 and 0 to 0.026, respectively. Three peaks of HEPEsm and HEPEpsd were observed at 13:00, 18:00, and 22:00. The peak value of HEPEsm increased with time, which exhibited a reverse trend to HEPEpsd. The spatial center of HEPEsm moved from the northwest of the study area to the center. The spatial center of HEPEpsd moved from the northwest of the study area to the southwest of the study area. The expansion area of HEPEsm was nearly 1.5 times larger than that of HEPEpsd. The expansion areas of HEPEpsd aggregated in the old downtown, in which the contribution of HEPEpsd was greater than 90%. Thus, this study introduced various source data to build an easier and reliable method to map total exceedance PM2.5 exposure risk. Consequently, exposure risk results provided foundations to develop PM2.5 pollution mitigation strategies as well as scientific supports for sustainability and eco-health achievement.

Current advances and future challenges of AIoT applications in particulate matters (PM) monitoring and control

Air pollution is at the center of pollution-control discussion due to the significant adverse health effects on individuals and the environment. Research has shown the association between unsafe environments and different sizes of particulate matter (PM), highlighting the importance of pollutant monitoring to mitigate its detrimental effect. By monitoring air quality with low-cost monitoring devices that collect massive observations, such as Air Box, a comprehensive collection of ground-level PM concentration is plausible due to the simplicity and low-cost, propelling applications in agriculture, aquaculture, and air quality, water resources, and disaster prevention. This paper aims to view IoT-based systems with low-cost microsensors at the sensor, network, and application levels, along with machine learning algorithms that improve sensor networks' precision, providing better resolution. From the analysis at the three levels, we analyze current PM monitoring methods, including the use of sensors when collecting PM concentrations, demonstrate the use of IoT-based systems in PM monitoring and its challenges, and finally present the integration of AI and IoT (AIoT) in PM monitoring, indoor air quality control, and future directions. In addition, the inclusion of Taiwan as a site analysis was illustrated to show an example of AIoT in PM-control policy-making potential directions.

Pollution Sources and Carcinogenic Risk of PAHs in PM1 Particle Fraction in an Urban Area

Airborne particles are composed of inorganic species and organic compounds. PM₁ particles, with an aerodynamic diameter smaller than 1 μm, are considered to be important in the context of adverse health effects. Many compounds bound to particulate matter, such as polycyclic aromatic hydrocarbons (PAH), are suspected to be genotoxic, mutagenic, and carcinogenic. In this study, PAHs in the PM₁ particle fraction were measured for one year (1/1/2018–31/12/2018). The measuring station was located in the northern residential part of Zagreb, the Croatian capital, close to a street with modest traffic. Significant differences were found between PAH concentrations during cold (January–March, October–December) and warm (April–September) periods of the year. In general, the mass concentrations of PAHs characteristic for car exhausts (benzo(ghi)perylene (BghiP), indeno(1,2,3-cd)pyrene (IP), and benzo(b)fluoranthene (BbF)) were higher during the whole year than concentrations of fluoranthene (Flu) and pyrene (Pyr), which originated mostly from domestic heating and biomass burning. Combustion of diesel and gasoline from vehicles was found to be one of the main PAH sources. The incremental lifetime cancer risk (ILCR) was estimated for three age groups of populations and the results were much lower than the acceptable risk level (1 × 10⁻⁶). However, more than ten times higher PAH concentrations in the cold part of the year, as well as associated health risk, emphasize the need for monitoring of PAHs in PM₁. These data represent a valuable tool in future plans and actions to control PAH sources and to improve the quality of life of urban populations.