The link between symptoms of office building occupants and in-office air pollution: the Indoor Air Pollution Index

The lack of an effective indoor air quality (IAQ) metric causes communication concerns among building tenants (the public),buildi ng managers (decision-makers),and IAQ investigators (engineers). The Indoor Air Pollution Index (IAPI) is developed for office buildings to bridge this communication discord. The index, simple and easily understood,employ s the range of pollutant concentrations and concentrations in the subject building to estimate a unitless single number,the IAPI,between 0 (lowest pollution level and best IAQ) and ten (highest pollution level and worst IAQ). The index provides a relative measure of indoor air pollution for office buildings and ranks office indoor air pollution relative to the index distribution of the US office building population. Furthermore,the index associates well with occupant symptoms,pe rcentage of occupants with persistent symptoms. A tree-structured method is utilized in conjunction with the arithmetic mean as the aggregation function. The hierarchical structure of the method renders not only one index value,but also several sub-index values that are critical in the study of an office air environment. The use of the IAPI for IAQ management is illustrated with an example. The decomposition of the index leads to the ranking of sampled pollutants by their relative contribution to the index and the identification of dominant pollutant(s). This information can be applied to design an effective strategy for reducing in-office air pollution

Olive tree, Olea europaea L., leaves as a bioindicator of atmospheric PCB contamination

Olive tree leaf samples were collected to investigate their possible use for biomonitoring of lipophilic toxic substances. The samples were analyzed for 28 polychlorinated biphenyls (PCB) congeners. Twelve congeners were detected in the samples. PCB-60, 77, 81, 89, 105, 114, and 153 were the most frequently detected congeners ranging from 32 % for PCB-52 to 97 % for PCB-81. Σ12PCBs concentration varied from below detection limit to 248 ng/g wet weight in the sampling area, while the mean congener concentrations ranged from 0.06 ng/g (PCB-128 + 167) to 64.2 ng/g wet weight (PCB-60). Constructed concentration maps showed that olive tree leaves can be employed for the estimation of spatial distrubution of these congener

Dilovası Endüstri Bölgesi ve Çevresinde Hava Kirliliğine Neden olan Organik ve İnorganik Kirleticilerin Düzeylerinin ve Kaynaklarının Belirlenmesi

TÜBİTAK ÇAYDAG15.11.2016Dilovası Türkiye?nin en büyük endüstriyel bölgelerinden biridir. Ilçede endüstriyel bölgeler veyerlesim yerleri iç içe bulunmakta, aynı zamanda ilçe yogun bir trafigin etkisi altındadır. Bununsonucu olarak bölgede kanser yüksek yogunlukla ölüm nedenlerinin basında gelmektedir. Buçalısma ile havadaki organik ve inorganik kirleticilerin seviyeleri, kaynakları ve muhtemelsaglık etkileri incelenmistir. Arazi çalısması Subat 2015 tarihinde baslamıs ve Subat 2016?desonlandırılmıs ve PAHlar ve PCBler zamansal ve mekânsal olarak incelenmistir. AyrıcaDilovası Belediyesi?nin bahçesinde kurulan sabit bir istasyonda sıralı PM2,5 örnekleyicikullanılarak EC/OC, 14C, metaller, çesitli anyon ve katyon derisimleri ve aynı istasyonda O3ve NOx miktarlarının zamansal degisimleri incelenmistir. Arazi çalısması 23 noktada PUF diskpasif örnekleyiciler kullanılarak yapılmıstır. Toplam PAH konsantrasyonu 4,2 ile 3842 ng m-3aralıgında degismektedir (ORT±SS; 285 ± 431 ng m-3). Fenantren en fazla elde edilen PAHtürü olurken, bunu floranten ve piren izlemektedir. Toplam PCB konsantrasyonu 177 ile 41781pg m-3 aralıgında degismektedir (ORT±SS; 4152 ± 6072 pg m-3). PCB-28, -18, -31 ve -33 enfazla elde edilen PCBlerdir. PMF sonuçlarına gör PAHlar için 5, PCBler içinse 4 farklı kaynakbelirlenmistir. Bunlar PAHlar için dizel ve benzinli araç emisyonları, biyokütle ve kömüryanması, demir-çelik endüstrisi gibi endüstriyel faaliyetlerden kaynaklı emisyonlar veyanmamıs petrol/petrol ürünleri emisyonları olarak belirlenmistir. PCBler içinse kaynaklardemir-çelik üretimi, kömür ve biyokütle yanması, teknik PCB karısımları ve endüstri olarakbelirlenmistir. Sabit istasyondan elde edilen PM2,5 kütlesini etkileyen faktörler yine PMFkullanılarak incelenmistir. Buna göre 6 faktör bulunmus, bunlar yol tozu (%23), tasıtemisyonları (%20), petrol yanması (%19), kömür yanması (%14), toz (%14), ikincil inorganikaerosol (%10) olarak belirlenmistir. Aynı sonuçlar kullanılarak bölgeyi etkileyen uzun mesafelikaynaklar potansiyel kaynak katkı fonksiyonu (PKKF) kullanılarak incelenmis ve Cezayir, BatıSahra, Rusya, Iran ve Lübnan örnekleme bölgesini etkileyen ülkeler olarak bulunmustur. Aynınoktalarda kuru çökelme örneklemesi de yapılmıs ve PAHlar için hesaplanan kuru çökelmeakısı degerlerinin 829 ile 7424 ng m?2 gün?1 aralıgında (2950 ± 1349 ng m?2 gün?1)degistigi gözlenmistir. PCBler içinse bu degerler 47,9 ile 535 ng m?2 gün?1 aralıgındadegismektedir (191 ± 102 ng m?2 gün?1). Sabit istasyonda ortalama EC, OC ve TCkonsantrasyonları sırasıyla 15,8±17,7, 2,8±2,3 ve 19,2±18,9 μg m-3 olarak bulunmustur.EC/OC konsantrasyonları özellikle kasım ayının ortasından baslayarak önemli derecede artısgöstermektedir. Bu sonuç sıcaklıgın düsmesine baglı olarak evsel ısınma faaliyetlerinin vebuna baglı olarak da karbon emisyonlarının artmasıyla, ayrıca karısım yüksekliginin düsmesive emisyonların atmosferde dagılamaması ile açıklanabilir. PAHlar, PCBler ve metaller içinsaglık riski degerlendirmesi yapılmıstır. En yüksek maruziyet-risk seviyeleri konsantrasyondegerleriyle paralel olarak PAHlar ve PCBler için sırasıyla kıs ve yaz döneminde tespitedilmistir. Tahmini kitlesel maruziyet-risk seviyelerinin hepsi ve ortalama maruziyet-riskseviyelerinin çogu kabul edilebilir kanserojen risk degerinin (10-6) üzerinde bulunmustur.Hedeflenen kanserojen metallere bakıldıgında ise Ni ve Pb için risk söz konusu degildir. Asiçin hesaplanan tahmini risk degerleri, bütün mevsimlerde kabul edilebilir risk degerininüzerinde bulunurken, Cd ve Co için hesaplanan tahmini risk degerlerinin bazıları kabuledilebilir degerin üzerinde bulunmustur. Bulunan risk degerleri kabul edilebilir degerin oldukçaüstündedir. Bu sonuçlar ile Dilovası havasının mevcut kirleticiler açısından insan saglıgı içinyüksek risk içerdigini söylemek mümkündür.Dilovasi is one of the most industrialized area in Turkey. In district, industrial sites are withinthe residential areas and the distinct is under the influence of heavy traffic. Consequently,the cancer became the main cause of death in Dilovasi. In present study, the levels oforganic and inorganic pollutants in the atmosphere, their sources and possible health riskswere investigated. The field campaign was started in February 2015 and completed inFebruary 2016, and spatial and temporal variations of PAHs and PCBs were investigated.EC/OC, 14C, anions and cations concentrations in samples collected using sequential PM2.5sampler, and NOx and O3 by active sampling were also investigated in sampling stationlocated in Dilovasi Municipality. Field studies were conducted using PUF disk sampler at 23sampling points. At all sites, total PAH concentrations ranged from 4.2 to 3842 ng m -3 with amean value of 285 ng m -3. They were dominated by phenanthrene, fluoranthene andpyrene. Total PCB concentrations were found to be between 177 and 41781 pg m -3 with amean value of 4152 pg m -3. They were dominated by low molecular weight (LMW)congeners such as PCB-28, PCB-18, PCB-31 and PCB-33. PMF was used to apportion thesources of PAHs and PCBs. Five sources were identified for PAHs including diesel andgasoline exhaust emissions, coal and biomass combustion, industrial activities such as ironsteelplants and unburned crude oil/petroleum products. For PCBs, the identified sources areiron-steel production, coal and biomass combustion processes, technical PCB mixtures andindustry. PMF was also used to find out the factors that affect the PM2.5 concentrations and 6factors were specified; road dust (23% contribution), vehicle emissions (20%), petroleumcombustion (19%), coal combustion (14%), dust (14%) and secondary inorganic aerosols(10%). Potancial Source Contribution Function (PSCF) was also applied to determine thelong-distance sources that affect the sampling region using these PMF results obtained forPM2.5. Algeria, West Sahara, Russia, Iran and Lebanon were found to be the possiblesources. Dry deposition studies were also conducted in same sampling points with passivesampling. The dry deposition fluxes obtained for PAHs were between 829 and 7424ng m−2 day−1 (2950 ± 1349 ng m−2 day−1) and for PCBs they were found between 47.9 and535 ng m−2 day−1 (191 ± 102 ng m−2 day−1). The average concentration of OC, EC and TCwere found as 15.8±17.7, 2.8±2.3 and 19.2±18.9 μg m-3, respectively. Especially afterNovember, the EC/OC concentrations found to be increased gradually. This can beattributed to domestic heating with decreasing temperature and lower mixing height inwinter. Risk assessment studies were also conducted for exposure to PAHs, PCBs andmetals. The highest exposure-risk levels were found in winter and summer for PAHs andPCBs, respectively. All of the estimated population risk and most of the average risk werefound to be the higher than general acceptable level (10-6). Among metals, no risk wasobserved for Ni and Pb. All of the estimated risks calculated for As for all seasons and someof risks for Cd and Co were higher than acceptable level. Estimated risks were substantiallyhigher than general acceptable levels. These results indicated that the atmosphere ofDilovasi has significant potential health risks for human beings in terms of these pollutants.Keywords: Dilovasi, PAHs, PCBs, Passive Sampling, EC/OC, Metals, Ions, Ozone, NOx,Positive Matrix Factorizatio

Application of artificial neural networks to predict prevalence of building-related symptoms in office buildings

Artificial neural networks (ANN) were constructed to predict prevalence of building-related symptoms (BRS) of office building occupants. Six indoor air pollutants and four indoor comfort variables were used as input variables to the networks. A symptom metric was used as the measure of BRS prevalence, and employed as the output variable. Pollutant concentration, comfort variable, and occupant symptom data were obtained from the Building Assessment and Survey Evaluation study conducted by the US Environmental Protection Agency, in which all were measured concurrently. Feed-forward networks that employ back-propagation algorithm with momentum term and variable learning rate were used in ANN modeling. Root mean square error and R2 value of the simple linear regression between observed and predicted output were used as performance measures. Among the constructed networks, the best prediction performance was observed in a one-hidden-layered network with an R2 value of 0.56 for the test set. All constructed networks except one showed a better performance than the multiple linear regression analysis

An exposure–risk assessment for potentially toxic elements in rice and bulgur

Rice and wheat are rich sources of essential elements. However, they may also accumulate potentially toxic elements (PTE). Bulgur, the popular alternative to rice in the eastern Mediterranean, is produced by processing wheat, during which PTE content may change. This study determined PTE concentrations in rice and bulgur collected from 50 participant households in the City of Izmir, Turkey, estimated ingestion exposure, and associated chronic-toxic and carcinogenic human health risks. Comparison of the determined concentrations to the available standard levels and the levels reported in the literature revealed that Cd, Co, and Pb in rice might be of concern. The estimated health risks of individual participants supported this result with exceedance of respective threshold or acceptable risk levels at the 95th percentile. Population risk estimates indicated that the proportion with higher than the threshold or acceptable risk is about 10%, 24%, and 12% for Cd, Co, and Pb in rice, respectively. Results of this study showed that health risks associated with PTE exposure through bulgur consumption are lower than those of rice, and below the threshold or acceptable risk levels

A health risk assessment for exposure to trace metals via drinking water ingestion pathway

A health risk assessment was conducted for exposure to trace metals via drinking water ingestion pathway for Province of İzmir, Turkey. Concentrations of 11 trace metals were measured in drinking waters collected from 100 population weighted random sampling units (houses). The samples were analyzed in atomic absorption spectrometry for arsenic, and inductively coupled plasma-optical emission spectrometry for Be, Cd, Co, Cr, Cu, Mn, Ni, Pb, V, and Zn. Questionnaires were administered to a participant from each sampling unit to determine drinking water consumption related information and demographics. Exposure and risks were estimated for each individual by direct calculation, and for İzmir population by Monte Carlo simulation. Six trace metals (As, Cr, Cu, Mn, Ni, and Zn) were detected in >50% of the samples. Concentrations of As and Ni exceeded the corresponding standards in 20% and 58% of the samples, respectively. As a result, arsenic noncarcinogenic risks were higher than the level of concern for 19% of the population, whereas carcinogenic risks were >10-4 for 46%, and >10-6 for 90% of the population

An exposure and risk assessment for fluoride and trace metals in black tea

Exposure and associated health risks for fluoride and trace metals in black tea were estimated. Fifty participants were randomly recruited to supply samples from the tea that they drink, and self-administer a questionnaire that inquired about personal characteristics and daily tea intake. Analyzed trace metals included aluminum, arsenic, barium, cadmium, cobalt, chromium, copper, manganese, nickel, strontium, and zinc. Fluoride and four metals (Al, Cr, Mn, Ni) were detected in all samples while barium was detected only in one sample. The remaining metals were detected in >60% of the samples. Fluoride and aluminum levels in instant tea bag samples were greater than in loose tea samples (p 1.0 × 10-6 even at the median level. According to sensitivity analysis, daily tea intake was the most influencing variable to the risk except for arsenic for which the concentration distribution was of more importance

Seasonal variation in drinking water concentrations of disinfection by-products in IZMIR and associated human health risks

Seasonal variation in concentrations of two different disinfection by-product groups, trihalomethanes (THMs) and haloacetonitriles (HANs), was investigated in tap water samples collected from five sampling points (one groundwater and four surface water sources) in İzmir, Turkey. Estimates of previously published carcinogenic and non-carcinogenic risks through oral exposure to THMs were re-evaluated using a probabilistic approach that took the seasonal concentration variation into account. Chloroform, bromoform, dibromochloromethane and dichloroacetonitrile were the most frequently detected compounds. Among these, chloroform was detected with the highest concentrations ranging from 0.03 to 98.4 μg/L. In tap water, at the groundwater supplied sampling point, brominated species, bromoform and dibromoacetonitrile, were detected at the highest levels most probably due to bromide ion intrusion from seawater. The highest total THM and total HAN concentrations were detected in spring while the lowest in summer and fall. The annual average total THM concentration measured at one of the surface water supplied sampling points exceeded the USEPA's limit of 80 μg/L. While all non-carcinogenic risks due to exposure to THMs in İzmir drinking water were negligible, carcinogenic risk levels associated with bromodichloromethane and dibromochloromethane were higher than one in million

Polycyclic and nitro musks in indoor air: A primary school classroom and a women's sport center

Indoor air gas and particulate-phase samples (PM2.5) were collected from a primary school classroom and a women's sport center because children are one of the sensitive population subgroups and women are frequent users of personal care products in addition to the high level of activity in this specific microenvironment. PM2.5 was collected with a Harvard impactor, and polyurethane foam was used for the gas phase. Samples were ultrasonically extracted, concentrated, and analyzed with a GC-MS. The mean gas-phase concentrations in the classroom ranged from 0.12 ± 0.2 ng/m3 for MK to 267 ± 56 ng/m3 for HHCB, while it was from 0.08 ± 0.10 ng/m3 for AHMI to 144 ± 61 ng/m3 for HHCB in the sports center. Particulate-phase average concentrations in the sports center ranged from 0.22 ± 0.11 ng/m3 for ATII to 1.34 ± 071 ng/m3 for AHTN, while it ranged from 0.05 ± 0.02 ng/m3 (musk xylene) to 2.50 ± 0.94 ng/m3 (HHCB) in the classroom. Exposure-risk assessment showed that inhalation route is most probably far less significant than the dermal route; however, it should be noted that the exposure duration covered in this study was not the larger fraction of the da

Indoor air quality in chemical laboratories

Chemical laboratories are special microenvironments, in which many pollutants may be found because of the large range and number of chemicals that can be used, while concentrations of some specific ones may relatively be elevated due to high source strengths depending on the type and the number of experiments conducted and the number of people working in the laboratory. Laboratories can be considered as public places for the students whereas they are occupational microenvironments for their staff (technicians, specialists and teaching/research assistants). Hence, laboratory indoor air quality (IAQ) is of importance due to chronic, toxic and carcinogenic health risks for the staff in addition to possible acute effects for both staff and students. This chapter presents background information regarding pertinent indoor air pollutants, factors that determine their concentrations, indoor environmental comfort, a review of the literature on indoor environmental quality in chemical laboratories and measures of IAQ management