PM10 Values Research Articles

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ABSTRACT To understand the contributions of a raw material storage site for iron ore, coal, limestone, and sinter to ambient air fine particulate matter (PM2.5), the concentrations and chemical fingerprints for resuspended and ambient air PM2.5 were compared. Investigations were done for 15 piles of raw materials, including 5 iron piles, 5 coal piles, 3 stone piles, and a single pile each for coke and sinter. Additionally, four sites, including A, B, C, and D, in the storage site surroundings were chosen to investigate the ambient air PM2.5 concentrations. The concentrations, compositions, and i and j values for PM2.5 varied significantly by season in the four sites under investigation. The chemical fingerprints of the PM2.5 showed that water-soluble ions were the most important component in all sites. Specifically, SO42– and NO3– were the predominant water-soluble ions in winter and summer, respectively. The most dominant components in the iron ore, coal, limestone, coke, and sinter piles were iron, carbon, Ca2+ and carbon, carbon and SO42–, and Fe and Ca2+, respectively. During the summer, PM2.5 concentrations ranged from 13.7–18.0 µg m–3, where the chemical composition of water-soluble ions, metals, carbon accounted for 54.2%, 5.7%, and 23.7% respectively. During winter, the concentrations ranged from 44.7–48.0 µg m–3, where the water-soluble ions, metals, carbon components accounted for 49.2%, 8.1%, and 17.4% respectively. From the chemical mass balance, the main sources of PM2.5 in sites B, C, and D were stationary sources, mobile sources, and secondary organic aerosols. To effectively address the air pollution threat associated with the surroundings of a raw material storage site, the environmental protection agency should formulate measures to effectively reduce the contribution of resuspended dust and other pollution sources to ambient air PM2.5.

АНАЛИЗ ЛДФ-ГРАММ У КРЫС СТОКА WISTAR В УСЛОВИЯХ МОДЕЛИРОВАНИЯ ГИПОКСИИ

The aim of the study was to assess the dynamics of the change in peripheral blood flow parameters of the blood vessels of the microcirculatory bed of the skin, with an additional analysis of the change in the amplitude of the frequency ranges of cutaneous blood flow, in the conditions of daily interval hypobaric training by laser Doppler flowmetry. The effect of interval hypobaric training course (seven days duration, exposure duration was 60 minutes) on the state of the microcirculatory bed of the skin was investigated to study the peculiarities of the formation and course of adaptive reactions in the vessels of the microcircuit. An analysis of microcirculation indicators using LDF diagnostics was carried out. Hardware – laser analyzer of capillary circulation «LACC-02» manufactured by LLC NPP «Lasma,» Moscow. The main basal blood flow indicators and microcirculation indicators were revealed when exposed to a 7-day course of interval hypobaric training. The statistical values of PM (constant perfusion parameter, units), σ (mean square deviation of amplitude of perfusion fluctuation, units) and Kv (coefficient of variation, %) provided an opportunity to analyze the general state of blood microcirculation. The amplitude-frequency spectrum of perfusion oscillations was also evaluated. With the help of a wavelet analysis (program 2.2.0.507, NPP Lasma, Russia), normalized amplitudes of blood flow fluctuations in frequency ranges were determined (endothelial due to nitric oxide activity (0.0095–0.02 Hz); neurogenic (0.02–0.06 Hz); myogenic (vasomosis) (0.06–0.2 Hz) and passive frequency range (cardiac (0.6–1.6 Hz), respiratory (0.2–0.6 Hz). Measurement of microcirculation parameters was carried out 3 times at the following stages of the experiment: before hypoxic exposure – point 0 (8 minutes), on the 1st day immediately after hypoxia exposure (8 minutes), and on the 7th day immediately after hypoxia session (8 minutes). The measurement area was the inner surface of the lower pelvic limb, the LDF sensor was located on the surface of the skin. The recording duration was 360 seconds. The hypoxic effect used in the study was implemented using hardware – an experimental hypobarocamera, in which the necessary conditions were created (negative pressure (–0.306kg/cm2)). The pressure in the chamber was reduced in stages: the first stage – a height of 1000 m with air dilution (–0.1 kg/cm2), duration 180 seconds; second stage – 2000 m rarefaction was (–0.21 kg/cm2) with the same duration; and the last stage is 3000 m high, which corresponds to a vacuum (–0.306 kg/cm2). A seven-day course of hypoxia did not cause significant changes in cutaneous blood flow in animals in group C (control), and in animals in group P (pseudo-hypoxic rats). However, it was found that in rats in group H (hypoxia) there was an increase in the coefficient of variation by 62.5 % (p≤ 0.05) on the 7th day of exposure, this may be due to the launch of adaptation mechanisms, which are necessary to adequately supply blood to cells, tissues and organs. When analyzing LDF-grams in rats of group C (control) and P (pseudo-hypoxed), there was no change in the perfusion parameter in the microcirculatory channel, but in group G (hypoxia) – PM (microcirculation index) significantly decreased by 47 % (p<0.05). In animals of groups C (control) and P (psvedo-hypoxed), there were no significant changes, according to the data of the wavelet analysis. However, in group H rats (hypoxia) on the 1st day of exposure, there was an increase in the frequency of the amplitude of oscillations in the myogenic range (0.06–0.2 Hz) on 18.72 % (p < 0.05) and passive frequency ranges (cardiac modulating factor (0.6–1.6 Hz) in the LDF-gram of the subjects on 14.97 % (p<0.05). Summing up the above, the results of the analysis of LDF-grams and the microcirculation index (PM) made it possible to establish a significant role in the possible use of the LDF-diagnostic method to evaluate the quality of methods for increasing the adaptive capabilities of the vessels of the skin microcirculatory bed and also central vessels. The dynamics of the frequency ranges of the vessels of nutritive blood flow in rats after a weekly course of interval hypobaric changed as follows: the role of passive factors, mainly the pulse wave from the arterial link, was increased by 24 % (p<0.05). The observed changes can be related to the enhancement of muscle cells in the vascular wall of the arterial vessels of the skin microcirculatory system, and as a result, the enhancement of the functioning of the circulatory system. By laser Doppler flowmetry, the dynamics of changes in peripheral circulatory indices were investigated with the assessment of changes in rats, with the study of the frequency ranges of cutaneous blood.

粤港澳大湾区PM2.5时空分布特征及其与气象要素的关系

细颗粒物（PM_2.5）污染不仅是现代社会城市化进程中的痛点，也是城市大气环境研究不可忽略的重要焦点。粤港澳大湾区作为世界级城市群，既是城市区域经济社会文化发展的重要体现，更是国家区域发展战略的重要构成与政策实施落脚点，其生态环境的优劣尤其受瞩目。对1999-2016年大湾区地表PM_2.5浓度栅格数据集进行了时空分布特征分析，其中空间自相关分析选取莫兰指数（Moran'I指数）作为度量；并利用多元线性回归模型探讨研究区内PM_2.5与气象要素之间关系。结果表明：粤港澳大湾区1999-2016年历年PM_2.5浓度呈先增加后减小的趋势，2008年为时间拐点，该时间节点之后空气质量显著提高，且1999、2009、2016三年，年平均PM_2.5浓度相似值趋于聚集分布。冷热点分析结果表明：热点区域集中于湾区行政核心区域范围内；冷点集中于核心边缘区域，空气质量较优。利用皮尔森相关分析最终筛选出实际蒸散量（aet）、太阳辐射（srad）、最低温度（tmmn）、蒸汽压（vap）、饱和水汽压差（vpd）、风速（ws）等6个气象因子，利用回归分析判断影响PM_2.5浓度时空分布的显著因子。结果表明：本研究区太阳辐射与PM_2.5浓度关系呈负相关，该结果与其他城市相关研究有较大差异，最小温度与PM_2.5浓度呈正相关，风速与PM_2.5浓度呈负相关，饱和水气压差与PM_2.5浓度呈正相关。;Fine particulate matter (PM_2.5) pollution is a serious environmental problem in the process of urbanization in modern society, and it is one of the most important research hotspots in the study of urban atmospheric environment. As a world-class urban agglomeration, Guangdong-Hong Kong-Macao Greater Bay Area is not only an important embodiment of the economic, social and cultural development of urban areas, but also an important component of the national regional development strategy and the foothold of policy implementation. Therefore, the ecological environment of this region is particularly important. The temporal and spatial distribution characteristics of PM_2.5 concentration data in Guangdong-Hong Kong-Macao Greater Bay Area from 1999 to 2016 were analyzed. Moran'I index was selected as the measurement in spatial autocorrelation analysis. The relationship between PM_2.5 concentration and meteorological factors in the study area was discussed by multiple linear regression model. The results indicated that the PM_2.5 concentration in Guangdong-Hong Kong-Macao Greater Bay Area increased firstly and then decreased from 1999 to 2016. The air quality improved significantly after the highest concentration in 2008, and the similar values of annual average PM_2.5 concentration tended to aggregate distribution in 1999, 2009 and 2016. The hot spots were concentrated in the administrative core area of the bay area; the cold spots were concentrated in the core edge area, where the air quality was better. Pearson correlation analysis (actual evapotranspiration (aet), solar radiation (srad), minimum temperature (tmmn), vapor pressure (vap), saturated vapor pressure difference (vpd), wind speed (ws)) was used to select six meteorological factors for regression analysis to evaluate the significant factors affecting the temporal and spatial distribution of PM_2.5 concentration. The results showed that there was a negative correlation between srad and PM_2.5 in this study area, which is quite different from other related studies in other cities. The tmmn and vpd were positively correlated with PM_2.5 concentration, while ws was negatively correlated with PM_2.5 concentration.

Marmara Bölgesi'nde Parçacık Madde ve Azot Dioksit Değerlerinin Kronik Bronşit İnsidansı ve Mortalite Üzerine Etkisi: Geriye Dönük Kohort Çalışması

Objective: The harmful effects of air pollution on human health have been proven by epidemiological studies. The aim of this study was to investigate the effect of two parameters that are determinants of air quality: particulate matter (PM10) and nitrogen dioxide (NO2) pollution levels on the incidence of chronic bronchitis and deaths due to air pollution. Material and Methods: We made the calculations by entering the 2018 population, mortality and PM10 and NO2 values of 11 provinces in the Marmara Region (İstanbul, Kocaeli, Edirne, Tekirdağ, Kırklareli, Sakarya, Bilecik, Bursa, Balıkesir, Çanakkale and Yalova) into the AirQ+ program [World Health Organization (WHO)]. All calculations made by AirQ+ are based on methodologies and dose-response functions established by epidemiological studies. In the calculations, the PM10 and NO2 limit value has been accepted as 20 μg/m3 . Results: It has been determined that the annual average PM10 level of all provinces in our study and the annual average NO2 level of eight provinces did not comply with the limit values recommended by WHO. Consistent with the particulate matter values, the highest incidence of chronic bronchitis among provinces are 43.71% [confidence interval (CI) 95%, relative risk (RR): 18.43-59.31] in Bursa and Sakarya 38.5% (CI 95%, RR: 15.71- 52.96); the highest number of people with chronic bronchitis attributed to air pollution has been found to be 2,349 (minimum 912-maximum 3,404) in İstanbul. Conclusion: Findings indicate the incidence of chronic bronchitis attributed to air pollution and the share of air pollution in deaths. Studies in different cities may contribute to the literature.

Describing Aerosol and Assessing Health Effects in Lima, Peru

The current study aims to discover the chemical-morphological characteristics of PM10 through the analysis of Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES) and Scanning Electron Microscopy (SEM), respectively, in the Metropolitan Area of Lima (MEAL) in the period from January 04 to 18, 2019. The study also aims to analyze the aerosol optical depth (AOD) in the period from January 1, 2014 to October 15, 2020. The effect of the PM2.5 concentrations on health in 2016 has also been quantified. The results indicate that the daily PM10 value of 50 μg/m3 recommended by the World Health Organization was exceeded on 75% of the monitored days. The morphological analysis of PM10 demonstrated the presence of particles of anthropogenic and geogenic origin. Particles from building activities and secondary aerosol formation were found, as well as particles associated with the resuspension of soil and marine aerosols. In 2016, 718, 1426, and 4295 cases of all-cause mortality, on average, could have been avoided in the MEAL if the annual average of PM2.5 would have decreased from 26.53 μg/m3 to 23.88 μg/m3, 21.22 μg/m3, or 10 μg/m3, respectively. In 2016, 1.58%, 3.14% and 9.47% of all causes are respectively attributed to fine aerosol (PM2.5) over 23.88 μg/m3, 21.22 μg/m3, and 10 μg/m3. Policy makers to legally reduce the PM2.5 concentrations in the MEAL could use this result.

Using Multiple Regression Model and RNN for Imputing the Missing Values of PM10 Datasets

The missing value in time series data is a scientific problem that should be solved by imputing these values by following some statistical techniques. This problem is more complex due to the missing values that existed in the dependent (response) variable. Particular matter (PM10) is a time series dataset used to scale air pollution as a dependent variable, while there are many types of pollutants used as independent variables. Malaysian datasets of PM10 and several climate pollutants are examined in this study. This study aims to impute the missing values for different missing rates in a dependent variable with minimum error. In this paper, the independent variables were supposed completed while the missing values have been replaced in different rates and different distributions within the dependent variable. Multiple linear regression (MLR) has been used as a traditional method to impute the different missing values of PM10. Recurrent neural network (RNN) is combined with MLR and used to impute the missing values of PM10. The results reflected that th hybrid method outperformed MLR for imputing the missing values of PM10. In conclusion, the hybrid method MLR-RNN can be used to impute the missing values of PM10 accurately compared to other traditional methods.

Impact of increase in urban sprawls representing five decades on summer-time air quality based on WRF-Chem model simulations over central-National Capital Region, India

The impact of urbanization on air quality is expected to vary based on how meteorology and the complex atmospheric chemistry would respond to prominent changes in land use land cover (LULC) taking place over several decades. The present study examines the aerodynamic impacts of an increase in the urban sprawls across the central National Capital Region, encompassing about 12,540 km2 area of India over fifty years for representative years 1972, 1981, 1993, 2003 and 2014 using WRF-Chem with equal emission inventories for each year. Subsequent to model validation for the study area, various air quality parameters are analyzed in the context of model prediction and increasing urban sprawls. Surface values and their spatial variations and vertical profiles during day and night are examined decade-wise and cumulatively over half a century for various pollutant species. In the various decades, where considerable area got urbanized, the model simulated a decrease of surface SO2 and PM10 values of about 20% and 40%, respectively, during both, night and daytime. In contrast, the O3 surface values show an increase of about 40% and 10% during night time and daytime, respectively. At higher levels, a significant increase in O3 and PM10 values is observed attributed to redistribution from changing wind speed and temperature pattern, which alters atmospheric mixing and the boundary layer. It is inferred that the aerodynamic impacts of urban sprawls influences the dispersal behavior of pollutants and requires further in-depth studies.

Impact of lockdown during COVID-19 pandemic on the air quality of North Indian cities

The World Health Organization, which proclaimed the COVID-19 a pandemic in early March 2020, imposed a partial lockdown by the Government of India on 21 March 2020. The aim of this investigation was to measure the change in air pollutants, including particulate matter (PM2.5 and PM10) and gaseous pollutants (NO2, CO and O3) during COVID-19 lockdown (25th March to 14th April 2020) across four major polluted cities in North India. In all region, PM2.5, PM10, NO2 and CO were significantly reduced while O3 has been shown mixed variation with increased in Agra and decreased in all other stations during lockdown. PM2.5 was reduced by ~20–50% and highly decreased in Noida. PM10 was most significantly decreased by 49% in Delhi. NO2 was reduced by ~10–70%, and high reduction was observed in Noida. Likewise, ~10–60% reduction was found in CO and most significantly decreased in Gurugram. However, an increased in O3 was observed in Agra by 98% while significantly reduced in other sites. Compared to the same timeframe in 2018–2019, PM2.5 and PM10 values for all sites were reduced by more than 40%.

Ultrafine urban particle measurements in Budapest and their airway deposition distribution calculation

Objectives The aim of this study was to provide particle number and mass deposition rates of submicron particles in the human airways as inputs for toxicology and other areas of aerosol science. Methods Scanning Mobility Particle Spectrometer was used to measure the number concentrations and size distributions of the ultrafine urban particles during summer and winter in Budapest. The Stochastic Lung Model (SLM) was applied to calculate number and mass deposition rates of the inhaled particles in different anatomical regions of the airways. Results Our calculations revealed that for the selected days in summer and winter with PM10 values below the health limit 4.7 and 18.4 billion particles deposited in the bronchial region of the lungs. The deposition in the acinar region of the lung was even higher, 8.3 billion particles for the summer day, and 33.8 billion particles for winter day. Conclusions Our results clearly demonstrate that large daily numbers of urban UFPs are deposited in the respiratory tract, which may play a key role in the health effects of particulate matter (PM) inhalation. Present results, connecting the ambient exposure parameters with the local burden of the airway epithelium, can be useful inputs of in vitro cell culture experiments. By the combination of urban UFP monitoring and numerical modeling of particle deposition with toxicological studies, the health risks of urban aerosols could be better assessed. The use of UFP data in addition to PM10 and PM2.5 in the epidemiological studies would also be indicated.

Assessment of the potential role of atmospheric particulate pollution and airborne transmission in intensifying the first wave pandemic impact of SARS-CoV-2/COVID-19 in Northern Italy

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which exploded in Wuhan (Hebei Region, China) in late 2019, has later spread around the world, causing pandemic effects on humans. During the first wave of the pandemic, Italy, and especially its Northern regions around the Po Valley, faced severe consequences in terms of infected individuals and casualties (more than 31,000 deaths and 255,000 infected people by mid-May 2020). While the spread and effective impact of the virus is primarily related to the lifestyles and social habits of the different human communities, environmental and meteorological factors also play a role. Among these, particulate pollution may directly impact the human respiratory system or act as virus carrier, thus behaving as potential amplifying factor in the pandemic spread of SARS-CoV-2. Enhanced levels of PM2.5 and PM10 particles in Northern Italy were observed over the 2-month period preceding the virus pandemic spread. Threshold levels for PM10 (< 50 μg/m3) were exceeded on 20–35 days over the period January–February 2020 in many areas in the Po Valley, where major effects in terms of infections and casualties occurred, with levels in excess of 80 μg/m3 occasionally observed in the 1–3 weeks preceding the contagious activation around February 25, 2020. Threshold values for PM2.5 indicated in WHO air quality guidelines (< 25 μg/m3) were exceeded on more than 40 days over the period January–February 2020 in large portions of the Po Valley, with levels up to 70 μg/m3 observed in the weeks preceding the contagious activation. In this paper, PM10 particle measurements are compared with epidemiologic parameters’ data. Specifically, a statistical analysis is carried out to correlate the infection rate, or incidence of the pathology, the mortality rate, and the case fatality rate with PM concentrations. The study considers epidemiologic data for all 110 Italian provinces, as reported by the Italian Statistics Institute, over the period 20 February–31 March 2020. Corresponding PM10 concentrations covering the period 15–26 February 2020 were collected from the network of air quality monitoring stations run by different regional and provincial environment agencies. The case fatality rate is found to be highly correlated to the average PM10 concentration, with a correlation coefficient of 0.89 and a slope of the regression line of (6.7 ± 0.3) × 10−3 m3/μg, which implies a doubling (from 3 to 6%) of the mortality rate of infected patients for an average PM10 concentration increase from 22 to 27 μg/m3. Infection and mortality rates are also found to be correlated with PM10 concentrations, with correlation coefficients being 0.82 and 0.80, respectively, and the slopes of the regression lines indicating a doubling (from 1 to 2‰) of the infection rate and a tripling (from 0.1 to 0.3‰) of the mortality rate for an average PM10 concentration increase from 25 to 29 μg/m3. Considerations on the exhaled particles’ sizes, their concentrations and residence times, the transported viral dose and the minimum infective dose, in combination with PM2.5 and PM10 pollution measurements and an analytical microphysical model, allowed assessing the potential role of airborne transmission through virus-laden PM particles, in addition to droplet and the traditional airborne transmission, in conveying SARS-CoV-2 in the human respiratory system. In specific circumstances which can be found in indoor environments, the number of small potentially infectious particles coalescing on PM2.5 and PM10 particles is estimated to exceed the number of infectious particles needed to activate COVID-19 infection in humans.

Multiple‐Factor Influence on Air Quality of Road Motor Vehicles Tail Number Limit in Administrative Area of Beijing, China

From December 2, 2013, to October 31, 2019 (total 2160 days), Beijing official air quality data was used as the research object. The article analyzes the end of days 4 and 9 and the end of the nonrestricted 4 and 9 days, working and nonworking days, restricted and nonrestricted working days, long holidays (Spring Festival and National Day), and nonlong holidays (short holidays other than the Spring Festival and National Day and working days) of AQI, PM2.5, PM10, SO2, CO, NO2, and O3. According to the statistical analysis of the data, the air quality of the 4 and 9 limit is worse than that of the non-4 and 9 limit. Motor vehicles restricted in traffic had an objective effect on air AQI, PM2.5, PM10, CO, and NO2, whereas there was almost no difference in O3. Some peak values of AQI, PM2.5, PM10, SO2, CO, and NO2 on nonrestricted working days were significantly higher than those on restricted working days. At the same time, there was a peak time of the impact of motor vehicles on AQI, PM2.5, PM10, SO2, CO, and NO2 in Beijing. This time should be between 3 and 5 days, or 72 and 120 hours.

Significant change in air quality parameters during the year 2020 over 1st smart city of India: Bhubaneswar.

Prevention of Coronavirus results in lockdown in India from 24 March 2020 to 31 May 2020. Eastern India, which is having a dense cluster of coal-fired power plants and home to many mines, mineral industries, has not shutdown power plants and coal mines during this lockdown period, though other industrial and vehicular emissions were almost zero. The present study attempts to find the change in various atmospheric pollutants during this lockdown period over an eastern tropical Indian station—Bhubaneswar, which is the first smart city proposed in smart city mission of Government of India. The study analyses hourly concentrations of PM2.5, PM10, NOX, O3, and CO for March–May 2019 and 2020. The study shows a significant increase (rather than decrease) in PM2.5 and PM10, increase in O3 and a decrease in CO and NOX during the lockdown period. Results are advocating the impact of transported pollution over the study area for maintaining the PM2.5 and PM10 values even during the lockdown situation.

Spatial identification and temporal prediction of air pollution sources using conditional bivariate probability function and time series signature.

Accurate identification of distant, large, and frequent sources of emission in cities is a complex procedure due to the presence of large-sized pollutants and the existence of many land use types. This study aims to simplify and optimize the visualization mechanism of long time-series of air pollution data, particularly for urban areas, which is naturally correlated in time and spatially complicated to analyze. Also, we elaborate different sources of pollution that were hitherto undetectable using ordinary plot models by leveraging recent advances in ensemble statistical approaches. The high performing conditional bivariate probability function (CBPF) and time-series signature were integrated within the R programming environment to facilitate the study's analysis. Hourly air pollution data for the period between 2007 to 2016 is collected using four air quality stations, (ca0016, ca0058, ca0054, and ca0025), situated in highly urbanized locations that are characterized by complex land use and high pollution emitting activities. A conditional bivariate probability function (CBPF) was used to analyze the data, utilizing pollutant concentration values such as Sulfur dioxide (SO2), Nitrogen oxides (NO2), Carbon monoxide (CO) and Particulate Matter (PM10) as a third variable plotted on the radial axis, with wind direction and wind speed variables. Generalized linear model (GLM) and sensitivity analysis are applied to verify and visualize the relationship between Air Pollution Index (API) of PM10 and other significant pollutants of GML outputs based on quantile values. To address potential future challenges, we forecast 3 months PM10 values using a Time Series Signature statistical algorithm with time functions and validated the outcome in the 4 stations. Analysis of results reveals that sources emitting PM10 have similar activities producing other pollutants (SO2, CO, and NO2). Therefore, these pollutants can be detected by cross selection between the pollution sources in the affected city. The directional results of CBPF plot indicate that ca0058 and ca0054 enable easier detection of pollutants' sources in comparison to ca0016 and ca0025 due to being located on the edge of industrial areas. This study's CBPF technique and time series signature analysis' outcomes are promising, successfully elaborating different sources of pollution that were hitherto undetectable using ordinary plot models and thus contribute to existing air quality assessment and enhancement mechanisms.

Chemical Characteristics and Identification of PM10 Sources in Two Lima Districts, Peru

This study evaluates the concentration of PM10 and PM2.5 and identification of source in the districts of San Juan de Lurigancho and Puente Piedra (PPD) in Lima-Peru. The samples were collected from April to May 2017 by the National Meteorology and Hydrology Service of Peru (Senamhi). The concentration of PM10 and PM2.5, measured by gravimetric techniques, exceeded the international (WHO) and national standards; with maximum values for PM10 and PM2.5 of 160 and 121.56 µg/ m3 in PPD and 295.06 and 154.58 µg/ m3 in SJL. Identification of sources by the Positive Matrix Factorization Model (PMF 5.0) and Principal Component Analysis (ACP), showed similar sources for both districts. In SJL, the combination of vehicular traffic and resuspension of soil dust, marine aerosol and iron and steel industry was determined, while in PPD the resuspension of soil dust, vehicular source, industrial activity and marine aerosol.

Air pollution and out-of-hospital cardiac arrest risk

Abstract Introduction Out-of-hospital cardiac arrest (OHCA) is a leading cause of death worldwide; it accounts for up to 50% of all cardiovascular deaths.It is well established that ambient air pollution triggers fatal and non-fatal cardiovascular events. However, the impact of air pollution on OHCA is still controversial. The objective of this study was to investigate the impact of short-term exposure to outdoor air pollutants on the incidence of OHCA in the urban area of Piacenza, Italy, one of the most polluted area in Europe. Methods From 01/01/2010 to 31/12/2017 day-by-day PM10 and PM2.5 levels, as well as climatic data, were extracted from Environmental Protection Agency (ARPA) local monitoring stations. OHCA were extracted from the prospective registry of Community-based automated external defibrillator Cardiac arrest “Progetto Vita”. OHCA data were included: audio recordings, event information and ECG tracings. Logistic regression analysis was used to estimate the association between the risk of OHC, expressed as odds ratios (OR), associated with the PM10 and PM2.5 levels. Results Mean PM10 levels were 33±29 μg/m3 and the safety threshold (50 μg/m3) recommended by both WHO and Italian legislation has been exceeded for 535 days (17.5%). Mean PM 5 levels were 33±29 μg/m3. During the follow-up period, 880 OHCA were recorded on 750 days; the remaining 2174 days without OHCA were used as control days. Mean age of OHCA patients was 76±15 years; male gender was prevalent (55% male vs 45% female; &amp;lt;0.001). Concentration of PM10 and PM 2.5 were significantly higher on days with the occurrence of OHCA (PM10 levels: 37.7±22 μg/m3 vs 32.7±19 μg/m3; p&amp;lt;0.001; PM 2.5 levels: 26±16 vs 22±15 p&amp;lt;0.001). Risk of OHCA was significantly increased with the progressive increase of PM10 (OR: 1.009, 95% CI 1.004–1.015; p&amp;lt;0.001) and PM2.5 levels (OR 1.012, 95% CI 1.007–1.017; p&amp;lt;0.001). Interestingly, the above mentioned results remain independent even when correct for external temperature or season (PM 2.5 levels: p=0.01 – PM 10 levels: p=0.002), Moreover, dividing PM10 values in quintiles, a 1.9 fold higher risk of cardiac arrest has been showed in the highest quintile (Highest quintile cut-off: &amp;lt;48μg/m3) Conclusions In large cohort of patients from a high pollution area, both PM10 and PM2.5 levels are associated with the risk of Out-of-hospital cardiac arrest. PM10 and PM2.5 levels and risk of OHCA Funding Acknowledgement Type of funding source: None

A Study on the Characteristics of Combustion Using Cow Manure Solid Fuel by Lab-scale Fluidized Bed Reactor

Interest in new and renewable energy is growing worldwide in response to global warming and the depletion of fossil fuels, and utilize and distribute various new and renewable energy sources in response to global trend. This study conducted a combustion experiment using cow manure solid fuel. The characteristics of the cow manure solid fuel, the emission gas and odor during combustion, and the ash from the combustion were analyzed. The results of the dried cow manure solid fuel, the water content, ash component, sulfur composition, and heavy metals were found to satisfied with the overall quality standard of the livestock excrement solid fuel. The results of the combustion exhaust, the experimental values of PM10, CO, and HCl were all higher than the emission acceptance criteria in the initial, mid and post stages of boiler operation, and concentrations of As, Cd and Hg are not detected. and the concentrations of Ni, Pb, Cr, fluoride compounds, nitrogen oxides and dioxin were lower than the standard. The results of the XRF analysis show that the main components were SiO2, P2O5, CaO and K2O. P2O5 in the ash can be a source of fertilizer, so it is deemed necessary to examine the possibility through further experiments. The analysis results of odor, showed that the concentration of ammonia, methylmercaptane, sulfuric acid, dimethyldysulfide and trimethylamine was higher than the emission acceptance criteria, while the concentration of dimethylacid was lower than the emission acceptance standard.

Characterization of particulate matter (PM) species in an area impacted by aggregate mining north of San Antonio, TX

Background: Aggregate and limestone mining in counties - especially Bexar and Comal, north of the city of San Antonio have been a cause of health concern recently. Aggregate mining particularly in residential areas can be problematic due to heavy truck traffic transporting the material resulting in vehicular air pollution as well.Methods: PM species were sampled at four locations north of San Antonio. The data was collected using a TSI Air Quality Sampler that sampled PM1, PM2.5, PM10, wind speed, wind direction, temperature, and relative humidity. Continuous data (1 minute averages) was recorded for the entire study period. The instrument was stationed at every location for a period of 7 days. The four locations were a ranch, open field, residential compound, and an elementary school. The sampling was conducted in August and September 2019.Results: The PM1 and PM2.5 levels were low at all the four sites in contrast to the PM10 levels. This suggests that the PM in Bexar and Comal Counties are impacted by mining activities primarily. For example, the seven day average for PM2.5 was about 8.6 ug/m3 at the ranch and PM10 values were around 15.8 ug/m3. PM species were highest at the residential compound due to the close proximity to an active mining area.Conclusions: Mining activities for limestone and aggregates should be limited in areas that are away from residential locations to minimize the respiratory exposure burden of the local population. More sampling needs to be done in other seasons as well.

Coal fly ash exposure and affective disorders in children aged 6 to 14.

Background: Fly ash, a waste product generated from burning coal for electricity, contains neurotoxic heavy metals, like arsenic and manganese. Fly ash is stored in landfills and surface impoundments, which may potentially expose communities to fugitive dust emissions. Small particles that escape from the storage facilities may cause affective disorders in children. Affective disorders include depression, bipolar disorder, and anxiety and have been estimated to impact about 7% of children according to the CDC. Although the toxicity of fly ash is great, limited information exists on the neurobehavioral impact of exposure to fly ash.Methods: We measure fly ash and neurobehavioral disorders in children living near coal ash storage facilities. Using personal modular impactors and lift tapes, we collect particulate matter that is analyzed for fly ash by scanning electron microscopy and energy dispersive x-ray spectroscopy. The child behavior checklist (CBCL) is used to collect information on neurobehavioral outcomes. Logistic regression was used to assess the association between fly ash and affective disorders. Odds ratios were adjusted for age and gender.Results: To date, we have enrolled 229 children, 45% who are female. The mean age is 10.8 years (SD=2.53) and 77% are white. The mean value of indoor PM10 is 22.6 µg/m3 (SD=17.6; Min=3.83, Max=164) and 45% of homes had fly ash. Overall, 14.4% of children had affective disorders. Children who were exposed to fly ash were 1.84 more times likely to have differences in mood and anxiety (AOR=1.84, 95% CI = 0.86-3.96).Conclusion: Although we have not yet collected our full sample of children (N=300), there may be a potential relationship between fly ash exposure and affective disorders in children. Fly ash storage is an emerging environmental health threat throughout the world. This study may provide impetus for understanding the health impacts from exposure and promote improved regulations.

Concentrations of chromium and lead in particulate matter less than 10 µm at a gym near a vehicular traffic intersection and its health implications in gym´s users in Bogotá, Colombia

Introduction: physical activity prevents from cardiovascular and mental diseases. To meet this goal, an appropriate external or internal environment must be available, where a good air quality is an important condition to develop the physical activity. Objective: to determine chromium and lead concentrations in particulate matter less than 10 µm at a gym near a vehicular traffic intersection and its health implications in gym´s users in Bogotá, Colombia. Methodology: for sixteen days (excluding Saturday, Sunday and holydays), from 7 a.m. to 9 p.m., at a gym near a vehicular traffic intersection, where public and private transport circulate, PM10 monitoring was carried out, using a Gil Air 3 personal sampling pump. Concentrations of lead and chromium were determined using graphite furnace atomic absorption spectroscopy, by analyzing PM10 filters, which were obtained from the personal sampling pump. Health implications were assessed by comparing the concentrations of the pollutants, against air quality guidelines of World Health Organization (WHO).Results: the daily average concentration of PM10, lead and chromium were 225,02 µg/m3 (SD: ± 152,54), 0,002 µg/m3 (SD: ± 0,002) and 0,50 ng/m3 (SD: ± 0,43) respectively. Conclusions: all the daily values of PM10 exceed the 24 hours air quality guideline of 50 µg/m3 as well as annual mean concentration of 20 µg/m3 established by WHO. Daily concentrations of lead did not exceed WHO’s standard, but chromium in 60% of the days with reading exceeded WHO’s annual average of 0,25 ng/m3. Although users of the gym only spend in average between 2 to 3 hours inside this place, they could be exposed to important concentrations of PM10 and chromium. These results are relevant because when users are doing physical activity, they are breathing more air that contains these pollutants, which could affect their health in the long term.

Forecasting Reference Evapotranspiration Using Time Lagged Recurrent Neural Network

The aim of this study is to employ a Time Lagged Recurrent Neural Network (TLRNN) model for forecasting near future reference evapotranspiration (ETo) values by using climate data taken from meteorological station located in Velestino, a village near the city of Volos, in Thessaly, centre of Greece. TLRNN is Multilayer Perceptron Neural Network (MLP-NN) with locally recurrent connections and short-term memory structures that can learn temporal variations from the dataset. The network topology is using input layer, hidden layer and a single output with the ETo values. The network model was trained using the back propagation through time algorithm. Performance evaluations of the network model done by comparing the Mean Bias Error (MBE), Root Mean Square Error (RMSE), Coefficient of Determination (R2) and Index of Agreement (IA). The evaluation of the results showed that the developed TLRNN model works properly and the forecasting ETo values approximate the FAO-56 PM values. A good proximity of predictions with the experimental data was noticed, achieving coefficients of determination (R2) greater than 75% and root mean square error (RMSE) values less than 1.0 mm/day. The forecasts range up to three days ahead and can be helpful to farmers for irrigation scheduling.