8-hour Average Concentration Research Articles

Study on formulation of standard limits for trichloroethylene and tetrachloroethylene in "Standards for indoor air quality（GB/T 18883-2022）" in China

There are clear indoor air pollution sources of trichloroethylene and tetrachloroethylene. A large number of epidemiological evidence has confirmed their carcinogenic toxicity and non-carcinogenic toxicity. Several countries and international organizations have paid attention to indoor air trichloroethylene and tetrachloroethylene. It has been also assessed that there should be certain potential health risk of indoor air trichloroethylene and tetrachloroethylene in China. Based on the latest research results of health risk assessment of indoor air trichloroethylene and tetrachloroethylene, the "Standards for indoor air quality (GB/T 18883-2022)" added trichloroethylene and tetrachloroethylene as indicators. The index limit of trichloroethylene is 6 μg/m3 for an 8-hour average concentration. The index limit of tetrachloroethylene is 120 μg/m3 for an 8-hour average concentration. The technical contents related to the determination of the standard limits of trichloroethylene and tetrachloroethylene in indoor air were analyzed and discussed, including the sources, the exposure, the health effects, the determination of the limit values, and the recommendations for standard implementation. It also proposed recommendations for the implementation of"Standards for indoor air quality (GB/T 18883-2022)".

Spatiotemporal prediction of O3 concentration based on the KNN-Prophet-LSTM model

In this paper, a prediction method based on the KNN-Prophet-LSTM hybrid model is established by using the daily pollutant concentration data of Wuhan from January 1, 2014, to May 3, 2021, and considering the characteristics of time and space. First, the data are divided into trend items, periodic items and error items by the Prophet decomposition method. Considering the advantages of the Prophet and the Long Short-Term Memory (LSTM) models, the trend items and periodic items are predicted by the Prophet model. The LSTM model is used to predict the error terms, and the K-Nearest Neighbor algorithm (KNN) is added to fuse the spatial and temporal information to predict the ozone (O3) concentration value day by day. To highlight the effectiveness and rationality of the KNN-Prophet-LSTM hybrid model, four groups of comparative experiments are set up to compare it with the single model Autoregressive Integrated Moving Average (ARIMA), Prophet, LSTM and the hybrid model Prophet-LSTM. The experimental results show that, (1) the daily maximum 8-hour average concentration of O3 in Wuhan has a significant periodic variation. The difference in the surrounding environment will lead to the difference in O3 concentration change in the region, and the O3 concentration change of similar stations will have a high similarity. (2) The Prophet decomposition algorithm decomposes the original time series, which can effectively extract the time series information and remove noise. Thus, the prediction accuracy is obviously improved. (3) Considering the spatial information of the surrounding sites by KNN algorithm, the accuracy of the model can be further improved. Compared with the baseline model ARIMA, the accuracy is improved by approximately 49.76% on mean absolute error (MAE) and 46.81% on root mean square error (RMSE) respectively. (4) The prediction effect of the mixed model is generally better than that of the single model and possesses a higher prediction accuracy.

The characteristics and change trends of air pollutants in Lanzhou City from 2014 to 2020

To study the distribution characteristics of atmospheric pollutants and their correlation with meteorological factors. The PM_(2.5), PM_(10), NO_2, SO_2, O_(3-1 h), O_(3-8 h) and CO daily average concentration data from 2014 to 2020 were obtained from Lanzhou Environmental Protection Bureau. The interannual changes of pollutants, monthly changes, seasonal changes and trend of spatial distribution were analysed. Spearman correlation analysis was performed on the relationship among pollutants. The main pollutants exceeding the standard in Lanzhou from 2014 to 2020 were PM_(10), PM_(2.5) and NO_2, average annual concentration of PM_(10), PM_(2.5), NO_2 and CO were decreasing year by year, and O_3 was increasing year by year. The monthly average concentration of PM_(10) was the highest in December, January, March and November were the second highest, and it was higher in February, April and May. The monthly change trends of PM_(2.5), NO_2, SO_2 and CO concentrations were the same, and the monthly change trends of the 1 hour average and daily maximum 8-hour average concentrations of O_3 were the same. The seasonal variation of atmospheric pollutant concentration was obvious, the concentrations of PM_(10), PM_(2.5), SO_2, NO_(2 )and CO were the highest in winter and the lowest in summer. O_3 concentration was the highest in summer and lowest in winter. Average annual concentration of 6 pollutants in different regions had statistically significant differences(H=750.40, 1112.99, 1410.05, 352.04, 360.17, 619.20, 729.52; P<0.001). Among them, the average annual concentration of PM_(10), PM_(2.5), SO_(2 )and O_(3-1 h) in Xigu District were the highest. PM_(10), PM_(2.5), NO_2, SO_2, CO average annual concentration were negatively correlated with temperature and wind speed(r_s=-0.423, -0.561, -0.395, -0.660, -0.569, -0.043, -0.094, -0.130, -0.172, -0.135), the concentration of PM_(10), PM_(2.5), O_3, SO_2 concentration were negatively correlated with humidity(r_s=-0.238, -0.121, -0.110, -0.094), only O_3 was positively correlated with temperature(r_s=0.486). The primary pollutants in Lanzhou from 2014 to 2020 were PM_(10), PM_(2.5) and NO_2.O_3 had an obvious upward trend year by year. The 6 pollutants had obvious seasonal changes and regional distribution characteristics. Some pollutants had the same homology, and meteorological factors affected each pollution. The concentration of the substance had an important influence. Relevant air pollution control measures should be formulated based on the main excessive pollutants, the monthly change trend of air pollution and the seasonal pollution characteristics, the same emission sources and geographical distribution characteristics should be considered, and the meteorological factors should be combined.

Evaluation of 8-hour average ground level Ozone in the city of Visakhapatnam and its impact on Human Well-being and Environment

Objectives: The present study was focused on the 8-hour average concentration of ground-level ozone in the city of Visakhapatnam for three years in a row, i.e. 2017, 2018, and 2019, and the effects of ozone on human life, the environment, and building materials. Method: The data of 8-Hour average groundlevel ozone concentration was collected for the study from the Central Control Room for Air Quality Management – All India, Central Pollution Control Board. Data available from the source were analyzed and compared to the NAAQS, India and the data was tabulated and showed specifically the number of times the 8 Hour average ground-level ozone surpassed in a month, for a clearer understanding. Findings: Study results show that the air quality of ozone levels during winters showed maximum unhealthy hours. The trends of maximum recorded 8-Hour average concentration of ozone levels in a day are also increasing year by year. Recommendations: It is recommended to be safe indoors during high ozone days for the people with comorbidities, children, Women in pregnant, people active in outdoor activities and it is further advised to reduce ozone exposure it\'s safe to wear washable 6-layer N99 and PM2.5 face mask during winters especially from November to February. Schools may be cautioned to discontinue the open-air physical activity during this period.Creating ozone forecasts accessible to the public is important, and the local and national laws should be strengthened to combat ozone pollution. Keywords: Asthma; contamination; ecosystems; urban vegetation; ozone; mortality

Impact of wood combustion on indoor air quality

The incomplete wood combustion in appliances operated in batch mode is a recognised source of both in- and outdoor airborne pollutants, especially particulate matter (PM). Data on pollutant levels and PM characteristics in households with wood burning devices in developed countries are scarce with most studies describing stove change out programmes or other intervention measures. The aim of the present study was to simultaneously evaluate indoor and outdoor concentrations of CO, CO2 and PM10 during the operation of wood burning appliances (open fireplace and woodstove) in unoccupied rural households. PM10 samples were analysed for water soluble inorganic ions, major and trace elements, organic carbon (OC), elemental carbon (EC), and detailed organic speciation. The CO 8-hour average concentrations did not exceed the protection limit despite the sharp increases observed in relation to background levels. During the open fireplace operation, PM10 levels rose up 12 times compared to background concentrations, while the airtight stove resulted in a 2-fold increase. The inhalation cancer risk of particulate bound PAHs in the room equipped with woodstove was estimated to be negligible while the long-term exposure to PAH levels measured in the fireplace room may contribute to the development of cancer. The excess lifetime cancer risk resulting from the particle-bound Cr(VI) exposure during the fireplace and woodstove operation was higher than 1.0 × 10−6 and 1.0 × 10−5, respectively. Levoglucosan was one of the most abundant individual species both indoors and outdoors. This study underlines air pollution hazards and risks arising from the operation of traditional wood burning appliances.

Characterisation of Carbon Monoxide concentrations associated with biomass fuel cooking in two university canteen settings, Kigali Rwanda

TPS 722: Low and middle income countries: indoor air pollutants, Exhibition Hall, Ground floor, August 28, 2019, 3:00 PM - 4:30 PM Biomass burning for cooking in occupational settings is widespread in Low and Middle Income Countries (LMICs), but remains a neglected research area. Adverse health consequences are associated with acute and chronic exposure to elevated Carbon Monoxide levels (CO) from biomass burning. The World Health Organisation (WHO) has an Indoor Air Quality Guideline (IAQ) for mean 8-hour CO concentrations of 10 mg/m3 (8.7 ppm) (WHO, 2010). In this study, CO levels were recorded at 1-minute intervals over a 48-hour period using Electrochemical Lascar EL-USB-CO monitors, at respirable height (150 cm), within two biomass fuel (charcoal, wood) canteen settings (staff, student) at the Nyarugenge Campus, University of Rwanda. Measurements in both canteens were obtained from the kitchens and adjoining serving areas. Hourly average CO concentrations were calculated using the hourly median (IQR), along with 8-hour average concentrations, to represent potential staff shift exposure. Two diurnal peaks, coinciding with cooking activities were observed, with peak 1-minute concentrations of 255.5 ppm (student kitchen) and 208.5 ppm (staff kitchen). Peak average 1-hour concentrations of 76.3 ppm (IQR: 57.8 – 109 ppm) and 76.3 ppm (IQR: 52.5 – 114 ppm), occurred within the morning cooking session, in the student and staff kitchens respectively. Calculated 8-hour average concentrations were highest during the morning staff shift in both the student kitchen (40.7 ppm, SD: 40.0 ppm) and the staff kitchen (34.7 ppm, SD: 35.8). Below WHO IAQ limit values were observed within the two serving areas, with two diurnal peaks coinciding with cooking activity. The CO levels observed from charcoal and wood cooking in this occupational setting are hazardous to human health and in exceedance of WHO IAQ guidelines. Further research into occupational personal exposure levels and health impacts of CO exposure among kitchen and serving staff is needed, alongside development of a clean fuel switching initiative.

Forecasting Surface O3 in Texas Urban Areas Using Random Forest and Generalized Additive Models

ABSTRACT We developed and evaluated three types of statistical forecasting models (quantitative, probabilistic, and classification) for predicting the maximum daily 8-hour average concentration of ozone based on meteorological and ozone monitoring data for six Texas urban areas from 2009 to 2015. The quantitative and probabilistic forecasting models were generalized additive models (GAMs), whereas the classification forecast used the random forest machine learning method. We found that for the quantitative forecasting models, five of the eight predictors (the day of week, day of the year, water vapor density, wind speed, and previous day’s ozone measurement) were significant at the α = 0.001 level for all urban areas, whereas the other three varied in significance according to the location. The quantitative forecasting for the 2016 ozone season agreed well with the associated measurements (R2 of ~0.70), but it tended to under-predict the ozone level for the days with the highest concentrations. By contrast, the probabilistic forecasting models showed little accuracy in determining the probability of concentrations exceeding policy-relevant thresholds during this season. The success rate for the random forest classification models typically exceeded 75% and would likely increase if the training data sets contained more extreme events.

Assessment of Japanese Photochemical Ozone Pollution by 8-hour Average Concentration

Assessment of Japanese Photochemical Ozone Pollution by 8-hour Average Concentration

Study of the regional air quality south of Mexico City (Morelos state)

Results from the first study of the regional air quality in Morelos state (located south of Mexico City) are presented. Criteria pollutants concentrations were measured at several sites within Morelos in February and March of 2007 and 2009; meteorological data was also collected along the state for the same time periods; additionally, a coupled meteorology–chemistry model (Mesoscale Climate Chemistry Model, MCCM) was used to gain understanding on the atmospheric processes occurring in the region. In general, concentrations of almost all the monitored pollutants (O3, NOx, CO, SO2, PM) remained below the Mexican air quality standards during the campaign; however, relatively high concentrations of ozone (8-hour average concentrations above the 60ppb level several times during the campaigns, i.e. exceeding the World Health Organization and the European Union maximum levels) were observed even at sites with very low reported local emissions. In fact, there is evidence that a large percentage of Morelos vegetation was probably exposed to unhealthy ozone levels (estimated AOT40 levels above the 3ppmh critical limit). The MCCM qualitatively reproduced ozone daily variations in the sites with an urban component; though it consistently overestimated the ozone concentration in all the sites in Morelos. This is probably because the lack of an updated and detailed emission inventory for the state. The main wind patterns in the region corresponded to the mountain–valley system (downslope flows at night and during the first hours of the day, and upslope flows in the afternoon). At times, Morelos was affected by emissions from surrounding states (Distrito Federal or Puebla). The results are indicative of an efficient transport of ozone and its precursors at a regional level. They also suggest that the state is divided in two atmospheric basins by the Sierras de Tepoztlán, Texcal and Monte Negro.

Polynomial Distributed Lag Models of Short Term Air Pollution Effects on Lung Function

P-040 Introduction: We recently reported a panel study (Moshammer et al. Eur Respir J, in press) on pupils at an elementary school in Linz, the capital of Upper Austria. A total of 163 children underwent lung function testing every 2–3 weeks and 11–12 times during one school year. Lung function testing was performed between 8:30 am and 11:00 am. Air pollution levels at the monitoring station in close vicinity of the school were usually below current and proposed limit values. Nevertheless the 8-hour average concentrations between midnight and school start at 8 o'clock in the morning had a significant effect on lung function results. No such associations was seen for the 8 hour mean values between 4 and 12 pm on the previous day indicating a rather acute and reversible effect. Polynomial distributed lag models have been successfully applied in time series studies on air pollution and daily deaths. In that case an increase of pollution followed by an early increase in death rates reaching a maximum and afterwards a minimum due to “harvesting” could well be modelled by a 3rd degree polynom. For the time course of lung function changes, however, shorter time intervals (e.g. half hour means) should be considered and more complex functions than 3rd degree polynoms. Two distinct modes of action with different time courses were expected: One very acute reaction due to nerval stimulation (reflectory vagal response) and a slower reaction mediated by an inflammatory tissue response. To model such a time course with at least 2 maxima polynoms of higher degree were necessary. Methods: Because lung function testing was on average performed at 9 o'clock and we had noticed an effect of the air pollution levels between 0 and 8 o'clock in the morning (and not the previous evening hours)we decided to study 34 half hour means (17 hours) as distributed lag before each individual lung function test. A general linear model (xtgee in STATA Vers. 9) was prepared as in the previous study: The logs of the various lung function parameters served as dependent variables, gender and the logs of age, height, and weight as independent variables and a forced autoregressive correlation of order 1 as the basic model. Additionally 5 to 7 variables were added that resulted from a transformation of the lag values applying polynomial models of the 4th to 6th degree. Results: Upon back-transformation polynomial models of the 5th and 6th degree gave the most consistent results, in accordance with the a priori hypothesis on the temporal course of the effect. As an additional check of internal consistency the air pollution levels at the half hour mean values with the maximal effect were added to the basic model.

Establishing Relevant Ozone Standards to Protect Vegetation and Human Health: Exposure/Dose-Response Considerations

For assessing the efficacy of a specific form of the National Ambient Air Quality Standard for O3, those exposure patterns that result in vegetation and human health effects must be identified. For vegetation, it has been found that the higher hourly average concentrations should be weighted more than the lower concentrations. Controlled human exposure work supports the suggestion that concentration may be more important than exposure duration and ventilation rates. It has been indicated in the literature that the current form of the federal O3 standard may not be appropriate for protecting vegetation and human health from O3 exposures. The proposed use of the cumulative index alone as a form of the standard may not provide sufficient protection to vegetation. An extended-period average index, such as a daily maximum 8-hour average concentration, may not be appropriate to protect human health because of the reduced ability to observe differences among hourly O3 concentrations exhibited within exposure regimes. For both vegetation and human health effects research, additional experimentation is required to identify differences in responses that occur when ambient-type exposure regimes are applied. Any standard promulgated to protect vegetation and human health from O3 exposures should consider combining cumulative exposure indices with other parameters so that those unique exposures that have the potential for eliciting an adverse effect can be adequately described.