Daily Particulate Matter Concentrations Research Articles

Electrostatic particle ionization for suppressing air pollutants in cage-free layer facilities

The increasing demand for cage-free (CF) poultry farming raises concern regarding air pollutant emissions in these housing systems. Previous studies have indicated that air pollutants such as particulate matter (PM) and ammonia (NH3) pose substantial risks to the health of birds and workers. This study aimed to evaluate the efficacy of electrostatic particle ionization (EPI) technology with different lengths of ion precipitators in reducing air pollutants and investigate the relationship between PM reduction and electricity consumption. Four identical CF rooms were utilized, each accommodating 175 hens of 77 wk of age (WOA). A Latin Square Design method was employed, with 4 treatment lengths: T1 = control (0 m), T2 = 12 ft (3.7 m), T3 = 24 ft (7.3 m), and T4 = 36 ft (11.0 m), where room and WOA are considered as blocking factors. Daily PM concentrations, temperature, and humidity measurements were conducted over 24 h, while NH3 levels, litter moisture content (LMC), and ventilation were measured twice a week in each treatment room. Statistical analysis involved ANOVA, and mean comparisons were performed using the Tukey HSD method with a significance level of P ≤ 0.05. This study found that the EPI system with longer wires reduced PM2.5 concentrations (P ≤ 0.01). Treatment T2, T3, and T4 led to reductions in PM2.5 by 12.1%, 19.3%, and 31.7%, respectively, and in small particle concentrations (particle size >0.5 μm) by 18.0%, 21.1%, and 32.4%, respectively. However, no significant differences were observed for PM10 and large particles (particle size >2.5 μm) (P < 0.10), though the data suggests potential reductions in PM10 (32.7%) and large particles (33.3%) by the T4 treatment. Similarly, there was no significant impact of treatment on NH3 reduction (P = 0.712), possibly due to low NH3 concentration (<2 ppm) and low LMC (<13%) among treatment rooms. Electricity consumption was significantly related to the length of the EPI system (P ≤ 0.01), with longer lengths leading to higher consumption rates. Overall, a longer-length EPI corona pipe is recommended for better air pollutant reduction in CF housing. Further research should focus on enhancing EPI technology, assessing cost-effectiveness, and exploring combinations with other PM reduction strategies.

Bushfire-smoke trigger hospital admissions with cerebrovascular diseases: Evidence from 2019-20 bushfire in Australia.

Exposure to ambient air pollution is strongly associated with increased cerebrovascular diseases. The 2019-20 bushfire season in Australia burnt 5.4 million hectares of land in New South Wales alone, with smoke so severe it affected cities in Argentina, 11,000 km away. The smoke emitted by bushfires consists of both gaseous and particle components. It is important to note that exposure to particulate matter has been shown to be linked to a heightened risk of stroke, which is the primary kind of cerebrovascular illness, as well as an increased likelihood of hospitalisations and mortality. However, the available data is inadequate in terms of documenting the response of patients diagnosed with a proven cerebrovascular illness to bushfire smoke. Additionally, there is a lack of information about the health effects associated with particulate matter throughout the bushfire season and on days when smoke was present in 2019 and 2020.Therefore, we aimed to determine the effects of (i) short-term air pollution triggered by bushfires and (ii) high smoke days in increasing the daily number of hospital admissions with cerebrovascular diseases. Hospitalisation data were accessed from the admitted patient dataset from seven local Government areas of Hunter New England Local Health District. The bushfire period was defined from 1 October 2019 to 10 February 2020, and a same period from 2018-19 as the control. High bushfire smoke days were days when the average daily concentration of particulate matter was higher than the 95th percentile of the control period. Poisson regression models and fixed effect meta-analysis were used to analyse the data. In total, 275 patients with cerebrovascular admissions were identified, with 147 (53.5%) during the bushfire (2019-20) and 128 (46.5%) in the control period (2018-19). There was no significant increase in daily admissions for cerebrovascular disease (Incidence Rate Ratio, IRR: 1.04; 95% CI: 0.81-1.34; p-value: 0.73), acute stroke (IRR: 1.15; 95% CI: 0.88-1.50; p-value: 0.29) or acute ischaemic stroke (IRR: 1.18; 95% CI: 0.87-1.59; p-value: 0.28), over the entire bushfire period. However, the high bushfire smoke days were associated with increased acute ischaemic stroke-related hospital admissions across lead 0-3 and the highest cumulative effect was observed with lead 0 (IRR:1.52; 95% CI: 1.01-2.29; p-value: 0.04). In addition, during the bushfire period, particulate matter, both PM10 and PM2.5 (defined as particulates that have an effective aerodynamic diameter of 10, and 2.5 microns, respectively), were also associated with increased acute ischaemic stroke admissions with a lag of 0-3 days. The results suggested a possible association between particulate matter and high smoke days with increased hospital admissions due to acute ischaemic stroke during the recent Australian bushfire season.

Ambient particulate matter, maternal thyroid function, and birth weight: A mediation analysis

BackgroundBirth weight (BW) is an indicator of fetal growth and development. Previous studies showed inconsistent results on the association of ambient particulate matter (PM) exposure with BW, and the role of maternal thyroid function has not been clarified. MethodsWe recruited 1711 gravidas between 2017 and 2019 in Henan, China. All participants were followed up until delivery. Daily concentrations of PM, including PM2.5 and PM10, were evaluated by using the spatial-temporal model. Maternal thyroid hormone (TH) levels were quantified by electrochemiluminescent microparticle immunoassay. Linear regression models were employed to examine the association among PM, BW, and maternal TH. Mediating effects of maternal TH interrelated with PM exposure on BW were investigated by causal mediation analyses. ResultsA total of 1049 gravidas were identified. We found that per 10 µg/m3 increase in PM2.5 and PM10 were associated with a decreased BW of 9.941 g, and 7.758 g (PM2.5: 95 %CI: −18.184, −1.698; PM10: 95 %CI: −14.436, −1.080). An inverse correlation of maternal FT4 levels with BW was found, with the pooled β of −319.983 g (95 %CI: −483.216, −156.750). We found a prominent positive correlation between gestational FT4 and PM exposure (PM2.5: β = 0.004, 95 %CI: 0.001, 0.007; PM10: β = 0.003, 95 %CI: 0.000, 0.006). Mediation analysis found that FT4 levels mediated the relationship between maternal PM exposure and BW, ranging from 5.55 % to 15.86 %. ConclusionsMaternal PM exposure may induce a reduction in newborn BW by affecting the maternal TH concentrations.

Prediction, modelling, and forecasting of PM and AQI using hybrid machine learning

This paper proposes a combination of hybrid models like Input Variable Selection (IVS), Machine Learning (ML), and regression method to predict, model, and forecast the daily concentrations of particulate matter (PM1, PM2.5, PM10) and Air Quality Index (AQI). A sensor placed in the centre of Craiova, Romania, provides a two-year dataset for training, testing, and validation phases. The analysis identifies the most important predictor variables for PM prediction and forecasting. The coefficient of determination (R2) values in this stage exceeded 0.95 (95%), indicating a strong correlation between PM concentrations. The performance of the proposed models is evaluated by objective measures, including root mean squared error (RMSE) and standard deviation (σ). RMSE ranged between 0.65 and 1 μg/m3, while σ has values between 2.75 and 4.1 μg/m3, reflecting a high level of precision and a successful performance of the proposed models. Furthermore, 13 multivariable-based PM models are developed in this study and adjusted using a hybrid Least Square - Decision Tree approach. The R2 values for these adjusted models range from 0.66 to 0.75, while the RMSE and σ vary between 8 and 9.1 μg/m3. Finally, a handled application for multistep-ahead time series forecasting is elaborated by combining the Nonlinear System Identification (NARMAX) approach with Decision Tree machine learning. This application allows for forecasting PM concentrations and AQI for the next periods. The R2 values obtained in this stage surpass 0.93, indicating almost a high level of accuracy. The RMSE ranged between 4.43 and 6.25 μg/m3, while the σ ranged between 4.44 and 6.26 μg/m3, further validating the precision of our forecasting model.

A novel approach for the prediction and analysis of daily concentrations of particulate matter using machine learning

Traditional air quality analysis and prediction methods depend on the statistical and numerical analyses of historical air quality data with more information related to a specific region; therefore, the results are unsatisfactory. In particular, fine particulate matter (PM2.5, PM10) in the atmosphere is a major concern for human health. The modelling (analysis and prediction) of particulate matter concentrations remains unsatisfactory owing to the rapid increase in urbanization and industrialization. In the present study, we reconstructed a prediction model for both PM2.5 and PM10 with varying meteorological conditions (windspeed, temperature, precipitation, specific humidity, and air pressure) in a specific region. In this study, a prediction model was developed for the two observation stations in the study region. The analysis of particulate matter shows that seasonal variation is a primary factor that highly influences air pollutant concentrations in urban regions. Based on historical data, the maximum number of days (92 days in 2019) during the winter season exceeded the maximum permissible level of particulate matter (PM2.5 = 15 μg/m3) concentration in air. The prediction results showed better performance of the Gaussian process regression model, with comparatively larger R2 values and smaller errors than the other models. Based on the analysis and prediction, these novel methods may enhance the accuracy of particulate matter prediction and influence policy- and decision-makers among pollution control authorities to protect air quality.

Differential effects of size-specific particulate matter on the number of visits to outpatient fever clinics: A time-series analysis in Zhuhai, China.

While many studies have investigated the adverse effects of particulate matter (PM), few of them distinguished the different effects of PM2.5, PM10, and coarse PM (PMc) on outpatients with fever. Our study aimed to estimate and compare the acute cumulative effects of exposure to three size-specific particles on the number of visits to outpatient fever clinics. To examine the association between daily PM concentrations and outpatients in fever clinics, a generalized additive Poisson model was applied, stratified by sex, age, and season. Our study included 56,144 outpatient visits in Zhuhai, from January 2020 to June 2021. On the current day, each 10 mg/m3 increment of PM10 and PMc were estimated to increase fever clinic visits by 1.74% (95% CI: 0.59%, 2.91%) and 4.42 % (2.30%, 6.58%), respectively. Cumulative effects enhanced from lag01 to lag05 for PM10 and PMc, and PMc had the strongest impact [ER = 8.92% (5.91%, 12.01%) at lag05]. Female outpatients and outpatients aged 14 years and above had an increased PM-related risk. During the cold season, significant effects could be observed for the three-size PM, while only PMc showed the impact during the warm season. Overall, the three size-specific PM exerted different effects on the fever clinic visits. Strategies to control the concentrations of PM are still necessary, especially against PM10 and PMc.

Assessing short term air quality trend in Malaysia based on air pollution index (APi)

Air Pollution Index (API) is used in Malaysia to determine the daily air quality status, which is calculated based on the daily concentrations of particulate matter (PM10), ground-level ozone (O3), carbon monoxide (CO), sulphur dioxide (SO2) and nitrogen dioxide (NO2). This study presents short-term air quality trends based on API from the 52 air quality monitoring stations nationwide between 2010 and 2016. The air quality data and meteorological conditions were obtained from the Department of Environment and used for the API calculation. The API value is classified into six categories, namely: Good (0-50), Moderate (51-100), Unhealthy (101-200), Very Unhealthy (201-300), Hazardous (301-500), and Emergency (above 500). The coefficient of variation (CV) and Mann-Kendall trend test (MK) were used to assess the API variation and trend in each air quality monitoring station. Between the study periods, the API values were largely varied. Observation at 32 air quality monitoring stations have shown significant but small increasing trends, while 12 stations showed significant decreasing trends, and the remaining 8 stations showed no significant trends. The frequency of exceedance (API>50) was used to assess the percentages of unhealthy days. The analysis has found that air quality in Klang Valley was experiencing the highest number of unhealthy days, while the two Malaysian states in Borneo (Sabah and Sarawak) to be relatively less polluted.

The Impact of Air Pollution and Aeroallergens Levels on Upper Airway Acute Diseases at Urban Scale

Air pollution (AP) represents one of the main environmental threats to public health and exposure to AP has been connected to upper airway (UA) disease. We evaluated the relationships between the ENT urgent referrals recorded at the Hospital of Padua and the daily levels of particulate matter (PM) as well as other environmental factors in a single year. Patients with UA disorders were included in the study group while those referred for facial trauma or foreign body inhalation formed the control group. Daily PM concentrations, meteorological data and the concentrations of the commonest aeroallergens were obtained. 6368 patients formed the study group and 910 the control one. The concentration of compositae allergens showed a positive effect on the total number of admissions (p = 0.001). PM10 did not demonstrate an effect on the total number of admissions or either the study or control groups admissions (p = 0.25). Alternaria positively influenced admissions of patients in the study group (p = 0.005). Significant relationships were found between the following: PM10 measured on the seventh day before A&E admission and rhinosinusitis (p = 0.007), PM10 on the fifth day and laryngitis (p = 0.01), PM10 on the second day and otitis media (p = 0.03), PM10 on the admission day and epistaxis (p = 0.0198). Our study confirms the causal relationship between aeroallergen concentration and ENT admissions. The levels of PM10 at specific days preceding A&E admission correlated with certain UA disorders. This study strongly points towards the harmful effects of pollution and climate change on UA disease.

The life expectancy benefits on respiratory diseases gained by reducing the daily concentration of particulate matter to attain different air quality standard targets: findings from a 5-year time-series study in Tianjin, China.

The short-term effects of particulate matter (PM) on years of life lost (YLL) have been confirmed by several studies; however, little attention has been paid to the effects of PM on life expectancy from respiratory diseases (RD), especially at the city level. A generalized additive model (GAM) was used to assess the associations between daily PM and YLL of the RD and chronic obstructive pulmonary diseases (COPD) in Tianjin from 2015 to 2019. Then the daily avoidable YLL, attributable fraction, and potential life expectancy benefits (PLEB) of RD and COPD by reducing the daily concentration of PM2.5 and PM10 to attain the Chinese and World Health Organization (WHO) air quality standard targets were estimated during 2015-2019. The stratified analyses by gender were also conducted. A total of 18279 RD and 7767 COPD deaths were included in this study. Per 10μg/m3 increments in PM2.5 and PM10 at lag1 were significantly associated with 0.83(95%CI: 0.11, 1.55) years and 0.57(95%CI: 0.06, 1.08) years increasing in YLL of total RD and 0.48 (95%CI: 0.07, 0.90) years and 0.30 (95%CI: 0.01, 0.60) years increasing in YLL of total COPD. If the daily PM2.5 and PM10 concentrations decreased to attain the Chinese and WHO air quality standard targets, 0.06-0.39years PLEB due to total RD and 0.08-0.53years PLEB due to COPD could be gained. And more PLEB on RD could be gained by decreasing the daily PM concentrations to attain the WHO air quality standard targets than that of attaining the Chinese air quality standard targets. These evidence-based findings emphasize the importance of the Chinese PM-related guideline updating and may have implications for PM-related policy-making.

Association between ambient particulate matter (PM2.5/PM10) and first incident ST-elevation myocardial infarction in Suzhou, China

Interests in evaluation of the effect of air pollution and weather conditions on cardiovascular disease have increased. However, the relationship between short-term particulate matter (PM) exposure and first incident ST-elevation myocardial infarction (STEMI) remains unclear. Medical records were collected from December 2013 to December 2016. A total of 1354 patients with first incident STEMI were included. The daily average of air pollution and weather conditions were calculated. In this case-crossover study, conditional logistic regression was performed to assess the association between daily concentrations of PM and first incident STEMI. The daily average of PM2.5 and PM10 were 58.9μg/m3 and 80.2μg/m3, respectively. In this case-crossover study, single-pollutant models showed that each 10μg/m3 increase in PM2.5 was associated with a percent change of 3.36, 95% confidence interval (CI): (1.01-5.77), or in PM10 percent change of 2.1%, 95%CI: (0.2-4.04) for patients with first incident STEMI. The association remained stable after adjusting for ozone (O3). The results from subgroup analysis showed the association slightly enhanced in women, elder patients, patients with history of diabetes, patients without history of smoking, and cold seasons. The p values were not significant between these strata, which may be due to small sample size. This investigation showed that short-term PM exposure associated with first incident STEMI in Suzhou. Given the effect of PM on the first incident STEMI, strategies to decrease PM should be considered.

Effects of ambient air pollution on emergency room visits of children for acute respiratory symptoms in Delhi, India.

The present study explored the association between daily ambient air pollution and daily emergency room (ER) visits due to acute respiratory symptoms in children of Delhi. The daily counts of ER visits (ERV) of children (≤15 years) having acute respiratory symptoms were obtained from two hospitals of Delhi for 21 months. Simultaneously, data on daily concentrations of particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3) and weather variables were provided by the Delhi Pollution Control Committee. K-means clustering with time-series approach and multi-pollutant generalized additive models with Poisson link function was used to estimate the 0-6-day lagged change in daily ER visits with the change in multiple pollutants levels. Out of 1,32,029 children screened, 19,120 eligible children having acute respiratory symptoms for ≤2 weeks and residing in Delhi for the past 4 weeks were enrolled. There was a 29% and 21% increase in ERVs among children on high and moderate level pollution cluster days, respectively, compared to low pollution cluster days on the same day and previous 1-6 days of exposure to air pollutants. There was percentage increase (95% CI) 1.50% (0.76, 2.25) in ERVs for acute respiratory symptoms for 10 μg/m3 increase of NO2 on previous day 1, 46.78% (21.01, 78.05) for 10 μg/m3 of CO on previous day 3, and 13.15% (9.95, 16.45) for 10 μg/m3 of SO2 on same day of exposure. An increase in the daily ER visits of children for acute respiratory symptoms was observed after increase in daily ambient air pollution levels in Delhi.

Scale-dependent effects of urban greenspace on particulate matter air pollution

Atmospheric particulate matter (PM) pollution has become a major global public health concern, particularly in urbanizing and rapidly developing nations such as China. Health effects from PM pollution include increases in cardiovascular and respiratory illnesses and problems during pregnancy and child development. Developing methods to mitigate PM pollution is a major priority for urban planning. Urban forests and other greenspaces are known to have both direct and indirect effects on PM concentrations, but uncertainty remains about how best to design greenspaces to reduce PM loads. This is partly because of contrasting effects that have been observed at different spatial scales. We used high-resolution satellite imagery and two years of daily PM concentration data from nine national air pollution monitoring stations in Zhengzhou, China, to examine the relative importance of seasonal cycling and greenspace effects on PM concentrations. We described patterns of urban greenspace at multiple spatial scales (buffer radii of 0.5 km – 3 km) using 15 different landscape pattern metrics. Multivariate methods (cluster analysis, principal component analysis) were used to process these into a smaller number of uncorrelated descriptors that described major patterns of variation in greenspaces. Linear mixed models were used to model seasonal cycling and greenspace effects on PM10 and PM2.5 concentrations for each spatial scale. We found that: 1) monthly mean, maximum, and standard deviations of concentrations of both PM2.5 and PM10 showed stationarity across seasons and months, being greatest in winter and smallest in summer; 2) urban greenspace patterns did not significantly affect PM2.5 concentrations; 3) increasing the overall abundance of greenspace, and percentage of large greenspace patches, at almost all scales significantly reduced PM10 concentrations; and 4) at larger spatial scales, increasing the amount of contact between the edges of greenspace patches and the surrounding urban area could reduce PM10 concentrations. In the context of urban planning and renewal, it may be possible to reduce PM10 pollution by carefully planning the shape and layout of greenspace networks across scales.

Determination of SARS-CoV-2 RNA in different particulate matter size fractions of outdoor air samples in Madrid during the lockdown

BackgroundPrevious studies described the presence of SARS-CoV-2 in outdoor air particulate matter (PM) in urban areas of northern Italy and USA. The city of Madrid was heavily affected by COVID-19 during March–June 2020. Also, this city usually displays high concentrations of PM under several atmospheric situations. This is mandatory to assess the presence of viral RNA in PM, as an indicator of epidemic recurrence. Our study was aimed at investigating the presence of SARS-CoV-2 RNA in outdoor air samples (on PM10, PM2.5 and PM1). MethodsSix samples of PM10, PM2.5 and PM1 were collected between the May 4th and 22nd 2020 in Madrid, on quartz fiber filters by using MCV high volume samplers (30 m3 h−1 flow) with three inlets (Digitel DHA-80) for sampling PM10, PM2.5 and PM1. RNA extraction and amplification was performed according to the protocol recently set by Setti et al.2020c in Italy. Up to three highly specific molecular marker genes (N1, N2, and RP) were used to test the presence of SARS-CoV-2 RNA. ResultsAfter RNA extraction and expression measurements of N1, N2 and RP genes from all the collected filters, no presence of SARS-CoV-2 RNA was observed. Control tests to exclude false positive results were successfully accomplished. ConclusionsNo presence of SARS-CoV-2 in quartz fiber filters samplers for PM10, PM2.5 and PM1 fractions was observed in our study carried out in Madrid during the month of May 2020. Nevertheless, the absence of viral genomes could be due to different factors including: limited social interactions and economic activities resulting in reduced circulation of the coronavirus, lower daily PM concentration in outdoor air, as well as to meteorological stability and higher temperature that characterize spring season. Further research should be carried out during winter, in presence of higher viral circulation and daily PM exceedances.

Acute effects of ambient particulate matter on daily emergency and outpatient visits for respiratory diseases among children aged 0-5 years in Beijing, China

PDS 68: Outdoor air pollution, mortality and morbidity, Exhibition Hall (PDS), Ground floor, August 26, 2019, 10:30 AM - 12:00 PM Background: Ambient particulate matter (PM) has been associated with respiratory diseases; however, few studies exist examining the impacts of PM on children who are particularly susceptible. Methods: Electronic records of daily emergency and outpatient visits for respiratory diseases among children aged 0-5 years from 1 January to 31 December in 2016 were collected from Beijing Children`s Hospital in Beijing, China. Daily concentrations of PM with aerodynamic diameter ≤ 2.5 μm (PM2.5) and ≤ 10 μm (PM10), and meteorology data at the same period were collected from Beijing Municipal Environmental Monitoring Center and a fixed monitoring site at Tsinghua University, respectively. Generalized Additive Models with a Poisson regression was used to estimate the associations between ambient PM and emergency and outpatient visits for respiratory diseases. Analyses were further performed for infants (<1 year) and children aged 1-5 years. Results: A total of 224,584 cases of respiratory diseases (35,930 infants and 188,654 children aged 1-5 years) were recorded. The daily mean levels of PM2.5 and PM10 were 71.14 μg/m3 and 103.01 μg/m3. We observed increases in emergency and outpatient visits of 0.18% (95% Confidence Interval (CI): -0.06%, 0.42%) and 0.28% (95%CI: 0.06%, 0.50%), in associations with a 10 μg/m3 increase in PM2.5 and PM10 at prior 1 day (lag1), respectively. Statistically significant cumulative effects of PM2.5 and PM10 (P<0.05) were observed among infants, with the largest effect estimates at lag03 (0.54%, 95%CI: 0.13%, 0.95%) and lag01 (0.42%, 95%CI: 0.16%, 0.68%), respectively, whereas no significant effects were observed among children aged 1-5 years. Conclusions: Our findings show the significant acute effects on daily emergency and outpatient visits for respiratory diseases of ambient PM among children and suggest the need for implementation of rigorous air quality policies to protect children’s health.

Ambient air pollution and in vitro fertilization treatment outcomes.

Is air pollution associated with IVF treatment outcomes in the USA? We did not find clear evidence of a meaningful association between reproductive outcomes and average daily concentrations of particulate matter with an aerodynamic diameter ≤2.5μm (PM2.5) and ozone (O3). Maternal exposure to air pollution such as PM2.5, nitrogen oxides, carbon monoxide or O3 may increase risks for adverse perinatal outcomes. Findings from the few studies using data from IVF populations to investigate associations between specific pollutants and treatment outcomes are inconclusive. Retrospective cohort study of 253 528 non-cancelled fresh, autologous IVF cycles including 230 243 fresh, autologous IVF cycles with a transfer of ≥1 embryo was performed between 2010 and 2012. We linked 2010-2012 National ART Surveillance System data for fresh, autologous IVF cycles with the ambient air pollution data generated using a Bayesian fusion model available through the Centers for Disease Control and Prevention's Environmental Public Health Tracking Network. We calculated county-level average daily PM2.5 and O3 concentrations for three time periods: cycle start to oocyte retrieval (T1), oocyte retrieval to embryo transfer (T2) and embryo transfer +14days (T3). Multivariable predicted marginal proportions from logistic and log-linear regression models were used to estimate adjusted risk ratios (aRR) and 95% CI for the association between reproductive outcomes (implantation rate, pregnancy and live birth) and interquartile increases in PM2.5 and O3. The multipollutant models were also adjusted for patients and treatment characteristics and accounted for clustering by clinic and county of residence. For all exposure periods, O3 was weakly positively associated with implantation (aRR 1.01, 95% CI 1.001-1.02 for T1; aRR 1.01, 95% CI 1.001-1.02 for T2 and aRR 1.01, 95% CI 1.001-1.02 for T3) and live birth (aRR 1.01, 95% CI 1.002-1.02 for T1; aRR 1.01, 95% CI 1.004-1.02 for T2 and aRR 1.02, 95% CI 1.004-1.03 for T3). PM2.5 was not associated with any of the reproductive outcomes assessed. The main limitation of this study is the use of aggregated air pollution data as proxies for individual exposure. The weak positive associations found in this study might be related to confounding by factors that we were unable to assess and may not reflect clinically meaningful differences. More research is needed to assess the impact of air pollution on reproductive function. None.

Living Well with Pollution? The Impact of the Concentration of PM2.5 on the Quality of Life of Patients with Asthma.

While the negative influence of environmental pollution on the respiratory system is well established, especially for people with bronchial hyper-reactivity, the impact of particulate matter on quality of life in asthma patients is not well understood. Three hundred adult asthma patients were recruited for a study; for each patient, the daily concentrations of particulate matter of 2.5 µm or less in diameter (PM2.5) were recorded from air quality monitoring stations. The study was conducted over two weeks. After two weeks, the patients filled out the Asthma Quality of Life Questionnaire (AQLQ), evaluating the quality of their lives throughout the monitored period. Patients exposed to a higher concentration of PM2.5 had significantly lower AQLQ scores. Every 10 µg/m3 of an increase in the concentration of PM2.5 resulted in a decrease of the AQLQ score by 0.16. All domains of quality of life (symptoms, activity limitations, emotional functioning, and environmental stimuli) assessed in the questionnaire were negatively affected by PM2.5. These findings provide an important argument in favor of educating physicians and patients and raising awareness about the detrimental health effects of air pollution. Improving the quality of life of people with asthma requires an immediate and substantial reduction of air pollution.

The Relationship between Daily Concentration of Fine Particulate Matter in Ambient Air and Exacerbation of Respiratory Diseases in Silesian Agglomeration, Poland.

The relationship between the worsening of air quality during the colder season of the year and respiratory health problems among the exposed population in many countries located in cold climates has been well documented in numerous studies. Silesian Voivodeship, a region located in southern Poland, is one of the most polluted regions in Europe. The aim of this study was to assess the relationship between daily concentration of particulate matter (PM: PM2.5 and PM10) in ambient air and exacerbations of respiratory diseases during the period from 1 January 2016 to 31 August 2017 in the central agglomeration area of Silesian Voivodeship. The study results confirmed a significant increase of daily fine particulate matter concentration in ambient air during the cold season in Silesian Voivodeship with a simultaneous increase of the number of outpatient visits and hospitalizations due to respiratory diseases. The moving average concentration was better suited for the modelling of biological response as a result of PM2.5 or PM10 exposure than the temporal lag of health effects. Each increase of dose expressed in the form of moving average concentration over a longer time leads to an increase in the daily number of respiratory effects. The highest risk of hospitalization due to respiratory diseases was related to longer exposure of PM expressed by two to four weeks of exposure; outpatient visits was related to a shorter exposure duration of 3 days.

Exploring atmospheric stagnation during a severe particulate matter air pollution episode over complex terrain in Santiago, Chile

A severe air quality degradation event occurred in the Santiago Metropolitan Area (SMA), Chile, in June 2014. Meteorological and air quality measurements from 11 stations in the area as well as numerical simulations using the Weather and Research Forecasting (WRF) model were used to explain the main reasons for the occurrence of elevated particulate matter (PM) concentrations. The conditions were characterized with formation of a coastal low in central Chile between the southeastern anticyclone and a high-pressure system over Argentina. At a local scale, these conditions generated a depression at the base of the inversion layer, an increase in the vertical thermal stability, lower humidity and low-wind conditions, which were conducive to a decrease in pollutant dispersion and insufficient ventilation of the polluted air. Measurements and simulations using the WRF model revealed a vertical structure of the boundary layer during these stagnant conditions and provided a basis for a trajectory analysis. The back-trajectory calculation showed that the transport of air parcels was contained in the valley during the highest concentrations. The analysis also enabled the definition of the threshold values of a simple indicator of air pollution (ventilation coefficient, VC), which confirmed the evolution of the episode and divided the observed daily concentrations into two groups, with one including values above the limits prescribed by the national air quality standards (NAQS) and the other including values below these limits. For the SMA, the daily PM concentrations above the NASQ limits were associated with an overall mean threshold value of VC below 500 m2 s−1 (for PM2.5) and 300 m2 s−1 (for PM10). To apply the VC analysis to other pollutants and different geographic locations, different threshold values should be evaluated.

The role of meteorologic factors and air pollution on the frequency of pediatric epistaxis.

Fluctuations in atmospheric temperature, humidity, and air pollution are associated with the incidence of epistaxis. To date, no study in the literature has evaluated the effect of air pollution and meteorologic conditions on the pediatric population. We aimed to evaluate the effect of meteorologic factors and air pollution on the frequency of epistaxis in children. Children presenting to an outpatient clinical setting at a tertiary care hospital during a 5-year period (July 1, 2009, to June 30, 2014) and diagnosed with epistaxis formed the study population. Daily temperature and humidity parameters and average daily atmospheric water vapor pressure, average daily concentration of particulate matter <10 μm in diameter, and sulfur dioxide readings were obtained. The distribution of daily parameters was analyzed. Of the 1,559 children with the primary diagnosis of epistaxis, data from 1,330 children were analyzed after excluding patients with coexisting pathologies. Positive correlations were found between the frequency of epistaxis and both the average daily temperature and the difference between the maximum and minimum daily temperature. There was a negative correlation between the epistaxis frequency and the average daily humidity, the difference between the maximum and minimum daily humidity, the average daily concentration of particulate matter, and the sulfur dioxide levels. Our findings suggest that epistaxis in children is related to high temperatures and low humidity.

Temporal variations in ambient particulate matter reduction associated short-term mortality risks in Guangzhou, China: A time-series analysis (2006–2016)

Numerous studies have reported associations between ambient particulate matter (PM) and daily mortality; however, little is known about temporal variations in ambient air pollution associated mortality risks, particularly in developing countries with limited long time-series air monitoring data.In present study, we assessed the associations and temporal relationships between ambient PM and daily mortality in Guangzhou, China, during 2006–2016. With this unique 11-year dataset, we related daily concentrations of PM with aerodynamic diameter < 2.5 μm (PM2.5), between 2.5 and 10 μm (PM10–2.5) and <10 μm (PM10) to daily mortality in Guangzhou. We applied overdispersed Poisson regression with adjustment for time trend and potential confounding factors. Multiple level sensitivity analyses were conducted to examine the robustness of main results.Between 2006 and 2016, annual concentrations of PM2.5 decreased by 50.8% to 27.0 μg/m3, of PM10–2.5 by 27.6% to 16.2 μg/m3, and of PM10 by 44.1% to 43.3 μg/m3 in Guangzhou. In this study, per 10 μg/m3 increases in mean concentrations at current day and 6 prior days of death (lag06), we observed increases in total mortality risks of 0.55% (95% Confidence Interval (CI): 0.24%, 0.86%) for PM2.5, 0.99% (95%CI: 0.48%, 1.50%) for PM10–2.5, and 0.44% (95%CI: 0.22%, 0.65%) for PM10. Stronger associations were observed for ambient PM on cardio-respiratory mortality and people at age ≥ 65 years. Despite drastic reductions in annual PM levels, PM2.5 associated cardiovascular and respiratory mortality risks remained significant at 1.26% (95%CI: 0.19%, 2.35%) and 1.91% (95%CI: 0.25%, 3.60%) during 2014–2016. Further, PM2.5 and PM10 associated respiratory mortality risks showed increasing trend over time (p-value = 0.03 for PM2.5).In summary, though ambient PM levels decreased substantially in Guangzhou in recent years, PM2.5 and PM10 associated cardio-respiratory mortality risks remained significant and respiratory mortality risks even increased. Our findings provide strong rationale for continuation of ambient air pollution control effort for public health protection in the future.