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      Air Quality Effects on Human Health and Approaches for Its Assessment through Microfluidic Chips

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          Abstract

          Air quality depends on the various gases and particles present in it. Both natural phenomena and human activities affect the cleanliness of air. In the last decade, many countries experienced an unprecedented industrial growth, resulting in changing air quality values, and correspondingly, affecting our life quality. Air quality can be accessed by employing microchips that qualitatively and quantitatively determine the present gases and dust particles. The so-called particular matter 2.5 (PM2.5) values are of high importance, as such small particles can penetrate the human lung barrier and enter the blood system. There are cancer cases related to many air pollutants, and especially to PM2.5, contributing to exploding costs within the healthcare system. We focus on various current and potential future air pollutants, and propose solutions on how to protect our health against such dangerous substances. Recent developments in the Organ-on-Chip (OoC) technology can be used to study air pollution as well. OoC allows determination of pollutant toxicity and speeds up the development of novel pharmaceutical drugs.

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          Most cited references158

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          The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants.

          Because human activities impact the timing, location, and degree of pollutant exposure, they play a key role in explaining exposure variation. This fact has motivated the collection of activity pattern data for their specific use in exposure assessments. The largest of these recent efforts is the National Human Activity Pattern Survey (NHAPS), a 2-year probability-based telephone survey (n=9386) of exposure-related human activities in the United States (U.S.) sponsored by the U.S. Environmental Protection Agency (EPA). The primary purpose of NHAPS was to provide comprehensive and current exposure information over broad geographical and temporal scales, particularly for use in probabilistic population exposure models. NHAPS was conducted on a virtually daily basis from late September 1992 through September 1994 by the University of Maryland's Survey Research Center using a computer-assisted telephone interview instrument (CATI) to collect 24-h retrospective diaries and answers to a number of personal and exposure-related questions from each respondent. The resulting diary records contain beginning and ending times for each distinct combination of location and activity occurring on the diary day (i.e., each microenvironment). Between 340 and 1713 respondents of all ages were interviewed in each of the 10 EPA regions across the 48 contiguous states. Interviews were completed in 63% of the households contacted. NHAPS respondents reported spending an average of 87% of their time in enclosed buildings and about 6% of their time in enclosed vehicles. These proportions are fairly constant across the various regions of the U.S. and Canada and for the California population between the late 1980s, when the California Air Resources Board (CARB) sponsored a state-wide activity pattern study, and the mid-1990s, when NHAPS was conducted. However, the number of people exposed to environmental tobacco smoke (ETS) in California seems to have decreased over the same time period, where exposure is determined by the reported time spent with a smoker. In both California and the entire nation, the most time spent exposed to ETS was reported to take place in residential locations.
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            Air pollution and lung cancer incidence in 17 European cohorts: prospective analyses from the European Study of Cohorts for Air Pollution Effects (ESCAPE).

            Ambient air pollution is suspected to cause lung cancer. We aimed to assess the association between long-term exposure to ambient air pollution and lung cancer incidence in European populations. This prospective analysis of data obtained by the European Study of Cohorts for Air Pollution Effects used data from 17 cohort studies based in nine European countries. Baseline addresses were geocoded and we assessed air pollution by land-use regression models for particulate matter (PM) with diameter of less than 10 μm (PM10), less than 2·5 μm (PM2·5), and between 2·5 and 10 μm (PMcoarse), soot (PM2·5absorbance), nitrogen oxides, and two traffic indicators. We used Cox regression models with adjustment for potential confounders for cohort-specific analyses and random effects models for meta-analyses. The 312 944 cohort members contributed 4 013 131 person-years at risk. During follow-up (mean 12·8 years), 2095 incident lung cancer cases were diagnosed. The meta-analyses showed a statistically significant association between risk for lung cancer and PM10 (hazard ratio [HR] 1·22 [95% CI 1·03-1·45] per 10 μg/m(3)). For PM2·5 the HR was 1·18 (0·96-1·46) per 5 μg/m(3). The same increments of PM10 and PM2·5 were associated with HRs for adenocarcinomas of the lung of 1·51 (1·10-2·08) and 1·55 (1·05-2·29), respectively. An increase in road traffic of 4000 vehicle-km per day within 100 m of the residence was associated with an HR for lung cancer of 1·09 (0·99-1·21). The results showed no association between lung cancer and nitrogen oxides concentration (HR 1·01 [0·95-1·07] per 20 μg/m(3)) or traffic intensity on the nearest street (HR 1·00 [0·97-1·04] per 5000 vehicles per day). Particulate matter air pollution contributes to lung cancer incidence in Europe. European Community's Seventh Framework Programme. Copyright © 2013 Elsevier Ltd. All rights reserved.
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              Atmospheric Chemistry and Physics: From Air Pollution to Climate Change

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                Author and article information

                Journal
                Genes (Basel)
                Genes (Basel)
                genes
                Genes
                MDPI
                2073-4425
                27 September 2017
                October 2017
                : 8
                : 10
                : 244
                Affiliations
                [1 ]German Federal Institute for Risk Assessment (BfR), German Centre for the Protection of Laboratory Animals (Bf3R), 10589 Berlin, Germany; marlon.schneider@ 123456bfr.bund.de
                [2 ]iSmart, Materials Genome Institute, Shanghai University (SHU), Shanghai 201800, China; gaoxinghua@ 123456t.shu.edu.cn
                [3 ]Department of Electrical Engineering, Kaunas University of Technology, 35212 Panevezys, Lithuania; darius.virzonis@ 123456ktu.lt
                [4 ]Department of Biochemistry, King Abdulaziz University, Jeddah 80203, Saudi Arabia; sdamiati@ 123456kau.edu.sa
                [5 ]Institute for Synthetic Bioarchitecture, Department of Nanobiotechnology, University of Natural Resources and Life Sciences, 1190 Vienna, Austria
                [6 ]Mathematics and Natural Sciences Department, The American University of Iraq, Sulaimani, Sulaymaniyah 46001, Iraq; rimantas.kodzius@ 123456auis.edu.krd
                Author notes
                [* ]Correspondence: Frank.Schulze@ 123456bfr.bund.de (F.S.); kodzius@ 123456envirola.com (R.K.); Tel.: +49-30-184123702 (F.S.); +370-64455494 (R.K.)
                Author information
                https://orcid.org/0000-0003-1137-7148
                https://orcid.org/0000-0001-9417-8894
                Article
                genes-08-00244
                10.3390/genes8100244
                5664094
                28953246
                4c032734-4f4c-4d09-935a-b300f4c49b5b
                © 2017 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 02 August 2017
                : 20 September 2017
                Categories
                Review

                air pollution,particulate matter (pm),pm2.5,microchip,organ-on-chip (ooc)

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