Epigenetic mechanisms of particulate matter exposure: air pollution and hazards on human health
Dulcemaría Gavito-CovarrubiasIvonne Ramírez‐DíazJosué Guzmán-LinaresIlhuicamina Daniel LimónDulce María Manuel-SánchezAlejandro Molina-HerreraMiguel Ángel Coral-GarcíaEstela AnastasioArely Anaya‐HernándezPrimavera López‐SalazarG. Juárez-DíazJ. Martínez‐JuárezJulián Torres-JácomeAlondra Albarado-IbáñezYgnacio Martínez‐LagunaCarolina MoránKarla Rubio
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Environmental pollution nowadays has not only a direct correlation with human health changes but a direct social impact. Epidemiological studies have evidenced the increased damage to human health on a daily basis because of damage to the ecological niche. Rapid urban growth and industrialized societies importantly compromise air quality, which can be assessed by a notable accumulation of air pollutants in both the gas and the particle phases. Of them, particulate matter (PM) represents a highly complex mixture of organic and inorganic compounds of the most variable size, composition, and origin. PM being one of the most complex environmental pollutants, its accumulation also varies in a temporal and spatial manner, which challenges current analytical techniques used to investigate PM interactions. Nevertheless, the characterization of the chemical composition of PM is a reliable indicator of the composition of the atmosphere, the quality of breathed air in urbanized societies, industrial zones and consequently gives support for pertinent measures to avoid serious health damage. Epigenomic damage is one of the most promising biological mechanisms of air pollution-derived carcinogenesis. Therefore, this review aims to highlight the implication of PM exposure in diverse molecular mechanisms driving human diseases by altered epigenetic regulation. The presented findings in the context of pan-organic cancer, fibrosis, neurodegeneration and metabolic diseases may provide valuable insights into the toxicity effects of PM components at the epigenomic level and may serve as biomarkers of early detection for novel targeted therapies.Keywords:
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Air pollution has become an urgent issue that affecting public health and people’s daily life in China. Social media as potential air quality sensors to surveil air pollution is emphasized recently. In this research, we picked up a case-2013 Eastern China smog and focused on two of the most popular Chinese microblog platforms Sina Weibo and Tencent Weibo. The purpose of this study is to determine whether social media can be capable to be used as ‘sensors’ to monitor air pollution in China and to provide an innovative model for air pollution detection through social media. Based on that, we propose our research question, how a salient change of air quality expressed on social media discussions to reflect the extent of air pollution. Hence, our research (1) determine the correlation between the volume of air quality-related messages and observed Air quality index (AQI) with the help of time series analysis model; (2) investigate further the impact of a salient change of air quality on the relationship between the people’s subjective perceptions regarding to air pollution released on the Weibo and the extent of air pollution through a co-word network analysis model. Our study illustrates that the discussions on social media about air quality reflect the level of air pollution when the air quality changes saliently. (Less)
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This study explored the relationship between the actual level of air pollution and residents’ concern about air pollution. The actual air pollution level was measured by the air quality index (AQI) reported by environmental monitoring stations, while residents’ concern about air pollution was reflected by the Baidu index using the Internet search engine keywords “Shanghai air quality”. On the basis of the daily data of 2068 days for the city of Shanghai in China over the period between 2 December 2013 and 31 July 2019, a vector autoregression (VAR) model was built for empirical analysis. Estimation results provided three interesting findings. (1) Local residents perceived the deprivation of air quality and expressed their concern on air pollution quickly, within the day on which the air quality index rose. (2) A decline in air quality in another major city, such as Beijing, also raised the concern of Shanghai residents about local air quality. (3) A rise in Shanghai residents’ concern had a beneficial impact on air quality improvement. This study implied that people really cared much about local air quality, and it was beneficial to inform more residents about the situation of local air quality and the risks associated with air pollution.
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This chapter presents the most common approaches of the regulatory framework for air quality and air dispersion modeling. The first major federal initiative in the United States to regulate air quality was the Clean Air Act of 1963. The Clean Air Act required the Environmental Protection Agency (EPA) to develop air quality standards. Air emissions are regulated by the states. They are responsible to ensure that the air quality standards are met. Each state (U.S.) and province (Canada) has its own air quality standards, based on the local conditions, industry, and the like. A screening technique in air dispersion modeling is the use of a simple model such as SCREEN3 to calculate the worst-case scenario resulting from a proposed air pollution source. When screening techniques predict the ambient air quality objectives to be exceeded, a refined modeling technique must be used to evaluate the proposed pollution source.
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The methodology of the recently developed Daily Air Quality Index (DAQx) and Long-term Air Quality Index (LAQx) is explained. Both indices consider air pollutants frequently monitored at long-term stations within official air pollution control networks. Therefore, they enable an assessment of the integral air pollution, which reflects the ambient air consisting of a mixture of air pollutants more realistic. Both air quality indices are impact related with respect to people. On the basis of results of extensive investigations in environmental medicine and toxicology, they quantify the impacts of a mixture of air pollutants, which is typical of the ambient air, on well-being and health of people in the form of six index classes and ranges of index values, respectively. To analyse the sensitivity of DAQx and LAQx, air pollutant data for the period 1995-2003 were used. They originate from selected stations within the official air pollution monitoring network in the South-West of Germany, which are characterised by different emission conditions.
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The paper examines the spatial distribution of air pollution in response to recent air quality regulations in Delhi, India. Air pollution was monitored at 113 sites spread across Delhi and its surrounding areas from July-December 2003. From the analysis of these data three important findings emerge. First, air pollution levels in Delhi and its surroundings were significantly higher than that recommended by the World Health Organization (WHO). Second, air quality regulations in the city adversely affected the air quality of the areas surrounding Delhi. Third, industries and trucks were identified as the major contributors of both fine and coarse particles.
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The air quality in Taiwan, at present, is determined by a pollution standard index (PSI) that is applied to areas of possible serious air pollution and Air Quality Total Quantity Control Districts (AQTQCD). Many studies, both in Taiwan and in other countries have examined the characteristics and levels of air pollution with PSI. This study uses air quality data collected from eight automatic air quality monitoring stations in an AQTQCD in central Taiwan and discusses the correlation between air quality variables with statistical analysis in an attempt to accurately reflect the difference of air quality observed by each monitoring station as well as to establish an air quality classification system suitable for the whole Taiwan. After using factor analysis (FA), seven air pollutants are grouped into three factors: organic, photochemical, and fuel. These three factors are the dominant ones in regards to the air quality of central Taiwan. Cluster analysis is used to classify air quality in central Taiwan into five clusters to present different characteristics and pollution degrees of air quality. This research results should serve as a reference for those involved in the review of air quality management effectiveness and/or the enactment of management control strategies.
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Air pollution is the result of economic growth and urbanization. Air pollution has been progressively recognized as a serious problem for cities, through widespread effects on health and well-being. There is less concern from stakeholders about greenness and air pollution mitigating factors in an urban area. This research targeted to indicate the spatial dissemination of greenery, air quality levels (PM2.5, PM10, CO2, and AQI), and exposure to air quality-related health risks for the people in the urban area.The data were collected by measuring air quality at transportation stations and manufacturing industries with Air visual pro, then observing and mapping greenness in the city within the administrative boundary by GIS (street greenery, forest, availability of greenness in the manufacturing industry), and lastly questionnaire and interview were employed for air quality-related health issues. Then, the air quality data were analyzed by using USAQI standards and health messages. Both quantitative and qualitative research approach had employed to explore air pollution levels, availability of greenness, and air quality-related health issues. Moreover, Health questionnaires and greenness were correlated with air quality levels by a simple linear regression model.The result indicated that there was unhealthy air quality in the transportation and manufacturing industries. The measured air quality showed in a range of 50.13-96.84 μg/m3 of PM2.5, 645-1764 ppm of CO2, and 137-179 Air quality index (AQI). The highest mean of PM2.5 and air quality concentrations at Addis Ababa transportation stations and manufacturing sites ranged between 63.46 and 104.45 μg/m3 and 179-326, respectively. It was observed with less street greenery and greenness available in residential, commercial areas, and manufacturing industries. The pollution level was beyond the limit of WHO standards. The result has shown a health risk to the public in the city, particularly for drivers, street vendors, and manufacturing industry employees. Among 480 respondents, 57.92% experienced health risks due to air pollution by medical evidence.High health risks due to industries and old motor vehicles in the city need to be reduced by introducing policies and strategies for low-carbon, minimizing traveling distance, encouraging high occupancy vehicles, and promoting a green legacy in the street network and green building.
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The Dynamic Air Pollution Prediction System (DAPPS) involves the development and integration of the following elements: Downscaling the current numerical urban-scale weather prediction to a finer spatial and temporal resolution, and establishing a comprehensive air pollutant emission inventory that will include industrial, motor vehicle and domestic emissions, and temporal variations in these emissions.The enhanced meteorological data and the emission inventory data will be used as inputs into a photochemical dispersion model, the Comprehensive Air Quality Model with Extensions (CAMx), to produce air pollution fields for the forecast meteorology.Local air quality affects how we live and what we breathe.Like the weather, it can change from day to daysometimes from hour to hour.The known and recognized health effects of air pollution include the increased risk of the exacerbation of respiratory symptoms such as increased asthma attacks and reduced lung function, increased hospital admissions for respiratory and cardio-vascular diseases, and increased mortality.An Air Quality or Air Pollution Index (API) is a quantitative tool through which air pollution data can be reported, providing information on how clean or polluted the air is, and the associated health concerns the public should be aware of.These indices usually focus on short-term health effects -those that can happen within a few hours or days of exposure to polluted air.A key feature of the DAPPS is that the final model output is a set of Air Pollution Indices.Several countries employ some type of air pollution index to communicate the quality of their air.Some of these systems rely on relating measured (monitored) or predicted concentrations of air pollutants to a numerical scale, for example ranging from 0 and 100.This scale may be enhanced by verbal descriptors such as high or moderate.The advantage of such a system is that the public does not have to interpret a number of different concentrations -one for each pollutant.They also do not need to recall that, the health effects of, for example 1 ppm of ozone is very different from those of 1 ppm of carbon monoxide.The simplistic use of a single index to reflect air pollution levels creates several difficulties.Different pollutants may have different health endpoints, information that may be lost through the use of a single index.Members of the public may also find it difficult to obtain details of how to translate a unified pollution index back into the disaggregated 'real' pollutant levels.In addition, it can be difficult to use an index to compare pollutant levels with national or international standards or guidelines, or with indices used in other countries.The use of a single standardized or unified scale doesn't solve the problems of how to report raised concentrations of a number of pollutants.The DAPPS proposes the development of a health-based Air Pollution Index as opposed to an Air Quality Index.The basic concept of this index is that of using a combination of modelled pollutant concentrations and exposure-response functions.The initial modelling output would be pollutant-specific numerical values indicating the degree of pollution in an area of the modelled domain.Normalising these values with exposure-response functions will result in normalised bands corresponding to a scale (for example, a scale of 1 to 10) and colour coding system that reflects the possible health impacts.Advice and information on possible health effects associated with each value on the scale would reflect information applicable to both the 'normal healthy' population and 'sensitive' groups within the exposed population (such as asthmatics, the aged or the very young).The index will be modified where possible to account for known additive and /or synergistic effects.
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