Estimate Emission Rates Research Articles

A satellite-data-driven framework to rapidly quantify air-basin-scale NO<sub><i>x</i></sub> emissions and its application to the Po Valley during the COVID-19 pandemic

Abstract. The evolving nature of the COVID-19 pandemic necessitates timely estimates of the resultant perturbations to anthropogenic emissions. Here we present a novel framework based on the relationships between observed column abundance and wind speed to rapidly estimate the air-basin-scale NOx emission rate and apply it at the Po Valley in Italy using OMI and TROPOMI NO2 tropospheric column observations. The NOx chemical lifetime is retrieved together with the emission rate and found to be 15–20 h in winter and 5–6 h in summer. A statistical model is trained using the estimated emission rates before the pandemic to predict the trajectory without COVID-19. Compared with this business-as-usual trajectory, the real emission rates show three distinctive drops in March 2020 (−42 %), November 2020 (−38 %), and March 2021 (−39 %) that correspond to tightened COVID-19 control measures. The temporal variation of pandemic-induced NOx emission changes qualitatively agrees with Google and Apple mobility indicators. The overall net NOx emission reduction in 2020 due to the COVID-19 pandemic is estimated to be 22 %.

Estimation of Emission and Inflow Rate to Water System for Tire-wear Particles in Korea

Estimation of Emission and Inflow Rate to Water System for Tire-wear Particles in Korea

Estimation of ship emission rates at a major shipping lane by long-path DOAS measurements

Abstract. Ships are an important source of SO2 and NOx, which are key parameters of air quality. Monitoring of ship emissions is usually carried out using in situ instruments on land, which depend on favourable wind conditions to transport the emitted substances to the measurement site. Remote sensing techniques such as long-path differential optical absorption spectroscopy (LP-DOAS) measurements can supplement those measurements, especially in unfavourable meteorological conditions. In this study 1 year of LP-DOAS measurements made across the river Elbe close to Hamburg (Germany) have been evaluated. Peaks (i.e. elevated concentrations) in the NO2 and SO2 time series were assigned to passing ships, and a method to derive emission rates of SO2, NO2 and NOx from those measurements using a Gaussian plume model is presented. A total of 7402 individual ship passages have been monitored, and their respective NOx, SO2 and NO2 emission rates have been derived. The emission rates, coupled with the knowledge of the ship type, ship size and ship speed, have been analysed. Emission rates are compared to emission factors from previous studies and show good agreement. In contrast to emission factors (in grams per kilogram fuel), the derived emission rates (in grams per second) do not need further knowledge about the fuel consumption of the ship. To our knowledge this is the first time emission rates of air pollutants from individual ships have been derived from LP-DOAS measurements.

The airborne contagiousness of respiratory viruses: A comparative analysis and implications for mitigation

The infectious emission rate is a fundamental input parameter for airborne transmission risk assessment, but data are limited due to reliance on estimates from chance superspreading events. This study assesses the strength of a predictive estimation approach developed by the authors for SARS-CoV-2 and uses novel estimates to compare the contagiousness of respiratory pathogens. We applied the approach to SARS-CoV-1, SARS-CoV-2, MERS, measles virus, adenovirus, rhinovirus, coxsackievirus, seasonal influenza virus and Mycobacterium tuberculosis (TB) and compared quanta emission rate (ERq) estimates to literature values. We calculated infection risk in a prototypical classroom and barracks to assess the relative ability of ventilation to mitigate airborne transmission. Our median standing and speaking ERq estimate for SARS-CoV-2 (2.7 quanta h−1) is similar to active, untreated TB (3.1 quanta h−1), higher than seasonal influenza (0.17 quanta h−1), and lower than measles virus (15 quanta h−1). We calculated event reproduction numbers above 1 for SARS-CoV-2, measles virus, and untreated TB in both the classroom and barracks for an activity level of standing and speaking at low, medium and high ventilation rates of 2.3, 6.6 and 14 L per second per person (L s–1 p–1), respectively. Our predictive ERq estimates are consistent with the range of values reported over decades of research. In congregate settings, current ventilation standards are unlikely to control the spread of viruses with upper quartile ERq values above 10 quanta h−1, such as SARS-CoV-2, indicating the need for additional control measures.

Greenhouse gas emission estimation from municipal wastewater using a hybrid approach of generative adversarial network and data-driven modelling

Greenhouse gas (GHG) emissions including carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) created via wastewater treatment processes are not easily modeled given the non-linearity and complexity of biological processes. These factors are also impacted by limited data availability making the development of artificial data generation algorithms, such as a generative adversarial network (GAN), useful for determination of GHG emission rate estimates (EREs). The main objective of this study was to develop a hybrid approach of using GAN and regression modelling to determine GHG EREs from a cold-region biological nutrient removal (BNR) municipal wastewater treatment plant (MWTP) in which the aerobic reactor has previously been established as the main GHG emission source. To our knowledge, this is the first application of GAN used for MWTP modelling purposes. The EREs were generated from laboratory-scale reactors used in conjunction with facility-monitored operating parameters to develop the GAN and regression models. Results showed that regression models provided reasonable EREs using parameters including hydraulic retention time (HRT), temperature, total organic carbon, and dissolved oxygen (DO) concentrations for CO2 EREs; HRT, temperature, DO and phosphate (PO43−) concentrations for CH4 EREs; and temperature, DO, and nitrogen (nitrite, nitrate, and ammonium) concentrations for N2O EREs. Additionally, the addition of 100 GAN-created virtual data points improved regression model metrics including increased correlation coefficient and index agreement values, and decreased root mean square error values. Clearly, virtual data augmentation using GAN is a valuable resource in supplementation of limited data for improved modelling outcomes. Genetic algorithm optimization was also used to determine operating parameter modifications resulting in potential for minimization (or maximization) of GHG emissions.

Evolution of formaldehyde (HCHO) in a plume originating from a petrochemical industry and its volatile organic compounds (VOCs) emission rate estimation

Large industrial facilities, such as petrochemical complexes, have decisive effects on regional air quality: directly due to their own hazardous volatile organic compounds (VOCs) emissions and indirectly due to their contribution to secondary air pollution. In South Korea, pronounced ozone and particulate matter issues have been reported in industrial areas. In this study, we develop a new top-down VOC emission rate estimation method using in situ airborne formaldehyde (HCHO) observations in the downwind plume of the Daesan Petrochemical Complex (DPC) in South Korea during the 2016 Korea–United States Air Quality (KORUS-AQ) mission. On May 22, we observed a peak HCHO mole fraction of 12 ppb after a transport time of 2.5 h (distance approximately 36 km) under conditions where the HCHO photochemical lifetime was 1.8 h. Box model calculations indicate that this elevated HCHO is mainly due to secondary production (more than 90% after 2 h of plume aging) from various VOC precursors including ethene, propene, and 1,3-butadiene. We estimate a lower limit for yearly DPC VOC emissions of 31 (±8.7) × 103 MT/year for HCHO precursors and 53 (±15) × 103 MT/year for all measured primary VOCs. These estimates are 1.5–2.5 times higher than the latest Korean emission inventories, KORUSv5. This method is beneficial not only by tracking the sources, sinks, and evolution of HCHO but also by validating existing emission inventories.

Atmospheric oil and natural gas hydrocarbon trends in the Northern Colorado Front Range are notably smaller than inventory emissions reductions

From 2008 to mid-2016, there was more than a 7-fold increase in oil production and nearly a tripling of natural gas production in the Colorado Denver–Julesburg Basin (DJB). This study utilized air samples collected at the Boulder Atmospheric Observatory (BAO) tower in southwestern Weld County in the DJB to investigate atmospheric mole fraction trends of methane and volatile organic compounds (VOCs). Elevated methane and propane mole fractions and low values (<1) in the ratio of i-pentane to n-pentane at BAO were found to be associated with flow patterns that transport air from the northeast (NE) to east (E) sector to the site, the direction where the primary locations of oil and natural gas (O&NG) extraction and processing activities are located. Median mole fractions of the O&NG tracer propane at BAO were 10 times higher than background values when winds came from the NE quadrant. This contrasts with lower mole fractions of O&NG-related constituents in air parcels arriving at BAO from the south, the direction of the major urban area of Denver. None of O&NG tracers, for example, methane and propane, show statistically significant trends in mole fraction (relative to the background) over the study period in air transported from the DJB. Also, longer term acetylene mole fraction changes were not seen in NE quadrant or south sector samples. A significant decline in the mole fraction ratio of i-pentane to n-pentane in the NE sector data provides evidence of an increasing influence of O&NG on the overall composition of VOCs measured at BAO, a change not seen in measurements from the south (urban) sector. These results suggest that O&NG emissions and resulting atmospheric mole fractions have remained relatively constant over 2008–2016. The behavior in the observations is in contrast to the most recent VOC emissions inventory. While the inventory projects O&NG total VOC emission reductions between 2011 and 2020, of –6.5% per year despite the large production increases, the best estimate of the propane emission rate of change for the DJB-filtered data during 2008–2016 is much smaller, that is, –1.5% per year.

Probable aerosol transmission of SARS-CoV-2 in a poorly ventilated courtroom.

There is increasing evidence of SARS‐CoV‐2 transmission via aerosol; the number of cases of transmission via this route reported in the literature remains however limited. This study examines a case of clustering that occurred in a courtroom, in which 5 of the 10 participants were tested positive within days of the hearing. Ventilation loss rates and dispersion of fine aerosols were measured through CO2 injections and lactose aerosol generation. Emission rate and influencing parameters were then computed using a well‐mixed dispersion model. The emission rate from the index case was estimated at 130 quanta h−1 (interquartile (97–155 quanta h−1). Measured lactose concentrations in the room were found relatively homogenous (n = 8, mean 336 µg m−3, SD = 39 µg m−3). Air renewal was found to play an important role for event durations greater than 0.5 h and loss rate below 2–3 h−1. The estimated emission rate suggests a high viral load in the index case and/or a high SARS‐CoV‐2 infection coefficient. High probabilities of infection in similar indoor situations are related to unfavorable conditions of ventilation, emission rate, and event durations. Source emission control appears essential to reduce aerosolized infection in events lasting longer than 0.5 h.

Lights and Shadows of the Voc Emission Quantification

Nowadays, Volatile Organic Compounds (VOC) are a class of air pollutants increasingly studied in the scientific literature, due to the well-known impact on human health and the environment. They can be emitted from different sources, including various types of industrial plants, such as oil refinery and petrochemical plants. For these reasons, the attention of control agencies on VOC emissions has increased and with it the request to quantify the total amount of VOC emitted by a plant, to monitor and to reduce this emission. However, several critical aspects still exist related to the classification of VOCs and their quantification. Indeed, different definitions of VOC are reported in scientific and technical literature. These diversities result in multiple strategies for quantifying the VOCs emitted by an industrial plant. Therefore, this paper reviews the available regulation about the quantification of VOC emitted from industrial plants, focusing on refineries and petrochemical plants, to underline some formal inconsistencies in VOC estimation procedures. From this comparison, it appears clear that VOC quantification can be addressed in different manners, obtaining a substantial non-uniformity of the VOC emission data. The data difference can conduct to a misleading estimation of emission rate.

Sensitivity of light duty vehicle tailpipe emission rates from simplified portable emission measurement systems to variation in engine volumetric efficiency

ABSTRACT Light-duty gasoline vehicle (LDGV) tailpipe emission rates can be quantified based on pollutant concentrations measured using portable emission measurement systems (PEMS). Emission rates depend on exhaust flow. For simplified and micro-PEMS, exhaust flow is inferred from engine mass air flow (MAF) and air-to-fuel ratio. For many LDGVs, MAF is broadcast via the on-board diagnostic (OBD) interface. For some vehicles, only indirect indicators of MAF are broadcast. In such cases, MAF can be estimated using the speed-density method (SDM). The SDM requires an estimate of the engine volumetric efficiency (VE), which is the ratio of actual to theoretical MAF. VE is affected by intra-vehicle variability in the engine load and inter-vehicle variability in engine characteristics (e.g., the type of valvetrain). The suitability of SDM-based estimates of MAF in conjunction with simplified and micro-PEMS has not been adequately evaluated. Therefore, the objectives are to: (1) quantify VE accounting for intra- and inter-vehicle variability; and (2) evaluate the accuracy of SDM-based vehicle emission rate estimation approaches. Seventy-seven naturally-aspirated LDGVs were measured using PEMS. For each vehicle, VE was estimated using three approaches: (1) constant VE calibrated to actual fuel use; (2) average estimates of VE for Vehicle Specific Power modes imputed from OBD data; and (3) modeled VE using multilinear regression (MLR). The MLR models were developed based on engine load and engine characteristics. The best model was selected based on various statistical diagnostics. When engines were under load, variability in VE was most sensitive to variations in engine load. During idling, VE differed between engines depending on engine characteristics. The constant and modeled VE estimation approaches enable the accurate estimation of microscale and mesoscale emission rates, with errors typically within ±10% compared to values imputed from OBD data. Thus, accurate emission rates can be obtained from simplified and micro-PEMS. Implications: Simplified and micro portable emission measurement systems (PEMS) enable widespread measurement of vehicle exhaust emission. As a cost saving measure, they estimate exhaust flow indirectly rather than via measurement, typically based on engine mass air flow (MAF). For some vehicles, MAF is not reported by the on-board diagnostic (OBD) system but can be inferred from other reported variables and volumetric efficiency (VE). However, VE is typically proprietary. Methods demonstrated here for estimating VE enable accurate quantification of emission rates, thereby enabling use of these PEMS for policy-relevant applications such as technology assessments, trends analysis, and emissions inventories.

Estimating gaseous pollutants in the air near Daura Refinery, Daura Power Plant and South of Baghdad Power Plant by calculating the fuel discharge

This study addresses estimation of emission rates and concentrations of SO2, CO and NO2 gases emitted from Daura Refinery (DR), Daura Power Plant (DPP) and South of Baghdad Power Plant (SBPP) by calculating the fuel discharge. The estimations were made by using the Gaussian plume model of dispersion at distances within 10 km from the pollution source for January, April, July and October 2017 under two stability conditions, slightly stable and moderately unstable. The effect of wind speed and direction as well as the ambient temperature of the surrounding air on the dispersion and transmission of air pollutants were also investigated. It was found that the unstable conditions are better for dispersing out atmospheric pollutants. The results showed that Zafarania District was the most affected by pollutants emitted from DR and BSPP while Daura District was more affected by DPP due to the prevailing wind direction. It was also found that an increase in wind speed leads to a decrease in the concentration of pollutants. The concentration of pollutants is inversely proportional to the height of the chimney, the speed of the gas leaving the chimney, while it is directly proportional to the diameter of the chimney. DPP has higher emission rates than DR and SBPP while at the surface level, the pollutants concentrations emitted from DR are greater than those emitted from DPP and SBPP.

Evaluating the Mobile Flux Plane (MFP) Method to Estimate Methane Emissions Using Large Eddy Simulations (LES)

AbstractThis study evaluates the efficacy of the mobile flux plane (MFP) method to derive methane emissions from oil and gas production fields using a first‐of‐its‐kind high‐resolution methane concentration data set. Transport and dispersion of methane emissions from seven hypothetical well pads generated with an oil field emission simulator is simulated every second at 10 m resolution using the Weather Research and Forecasting (WRF) model in large eddy simulation mode. The time varying WRF‐generated methane concentration data set is sampled by a simulated MFP system downwind of the seven well pads at five sampling distances of 50, 75, 100, 125, and 150 m. Several key findings highlight the significant variability in MFP emission rate estimates induced by atmospheric turbulence and variable source emission rates. Natural atmospheric turbulence alone was found to generate significant variability (33%–75%) in the MFP emission estimates with constant emission rates at the source location. It was also found that turbulent wind speed fluctuations over the duration of a transect can also affect MFP estimates up to about ±50% through convergence (divergence) that increases (decreases) methane concentrations, and by its effect on the assumption of steady winds over the duration of the transect. It was further found that the MFP method typically estimated about 19%–33% and 51%–75% of known site emission rates using the trapezoidal and Gaussian fit integration methods, respectively. Thus, methane concentrations would need to be measured to a much higher elevation to generate robust and accurate methane emission rate estimates.

Inverse AERMOD and SCIPUFF Dispersion Modeling for Farm-Level PM10 Emission Rate Assessment

HighlightsAERMOD and SCIPUFF were employed to back-calculate farm-level PM10 emission rates based on inverse modeling.Both AERMOD and SCIPUFF did not capture the diurnal and seasonal variations of farm-level PM10 emission rates.AERMOD modeling results were affected by wind speed, with higher wind speed leading to higher emission rates.Higher numbers of receptors and PM10 measurements with greater time resolution may be recommended in the future.Abstract. Air pollutant emissions from animal feeding operations (AFOs) have become a serious concern for public health and ambient air quality. Particulate matter with aerodynamic equivalent diameter less than or equal to 10 µm (PM10) is one of the major air pollutants emitted from AFOs. To assess the impacts of PM10 emissions from AFOs, knowledge about farm-level PM10 emission rates is needed but is challenging to obtain through field measurements. The inverse dispersion modeling approach provides an alternative way to estimate farm-level PM10 emission rates. In this study, two dispersion models, AERMOD and SCIPUFF, were employed to back-calculate farm-level PM10 emission rates based on hourly PM10 concentration measurements at four downwind locations in the vicinity of a commercial egg production farm in the southeast U.S. Onsite meteorological data were simultaneously recorded using a 10 m weather tower to facilitate the dispersion modeling. The modeling results were compared with PM10 emission measurements from two layer houses on the farm. Single-area source, double-area source, and double-volume source were used in AERMOD, while only single-point source was used in SCIPUFF. The inverse modeling results indicated that both SCIPUFF and AERMOD did not capture the diurnal and seasonal variations of the farm-level PM10 emission rates. In addition, the AERMOD modeling results were affected by wind speed, and higher emission rates may be predicted at higher wind speeds. The single-point source for SCIPUFF, the plume rise simplification for AERMOD, and insufficient concentration measurement resolution in response to temporal changes in wind direction may have added uncertainties to the modeling results. The results of this study suggest that more receptors covering more representative downwind locations should be considered in future modeling for farm-level emissions assessment. Moreover, ambient data collection with greater time resolution (e.g., less than one hour) is recommended to capture diurnal and seasonal patterns more rigorously. Only in this way can researchers achieve a better understanding of the effectiveness of inverse dispersion modeling for estimation of pollutant emission rates. Keywords: AERMOD, Animal feeding operations, Egg production, Farm-level emission rate, Inverse dispersion modeling, PM10, SCIPUFF.

Environmental Impact Assessment Caused by Emissions from the State Enterprise ‘Kolky Forestry’

Purpose. Estimation of the impact of State Enterprise ‘Kolky Forestry’ pollutants emissions on atmospheric air. Methods. Environmental chemical analysis, instrumental analysis, computational method, generalization and systematization methods, comparativeanalytical, descriptive and cartographic methods. Results. The sources of pollutant emissions of State Enterprise ‘Kolky Forestry’ were examined. During the equipment operation, the following substances are emitted into the atmospheric air: iron oxide, manganese and its compounds, nitrogen oxides (in terms of dioxide), carbon monoxide, sulfur dioxide, substances in the form of chemically undifferentiated suspended solids, hydrogen sulfide, benzene, hydrocarbons, xylene, toluene, sodium hydroxide, greenhouse gases (methane, carbon dioxide, dinitrogen oxide, NMVOCs). The assessment of pollutants emissions impact on the state of air pollution is carried out according to the results of the estimated dispersion of pollutants in the air and data obtained via instrumental research methods. The concentration of pollutants in the atmospheric surface layer at the borderline of the 100 m sanitary bufferis as follows: nitrogen oxides (in terms of dioxide) – 0.18 MPC, suspended solids – 0.55 MPC, sodium hydroxide (lye, caustic soda) – 0.10 MPC. Conclusions. The estimationof pollutants concentrationon the basis of actual and estimated emission rates revealed that harmful substances concentration in the atmospheric surface layer does not exceed the maximum permissible concentration and does not have a negative effect on human health and the state of the environment at the borderline of the sanitary buffer.

Estimation of greenhouse gas and odour emissions from a cold region municipal biological nutrient removal wastewater treatment plant

Seasonal temperature variations in cold regions worldwide lead to variable gas emissions from municipal wastewater treatment plants (MWTPs) due to changing wastewater temperatures in open-to-air treatment processes. The objective of this study was to determine the greenhouse gas (including carbon dioxide, CO2; methane, CH4; and nitrous oxide, N2O) and odour (including ammonia, NH3; and hydrogen sulphide, H2S) emission rate estimates (EREs) from the open-to-air processes of a biological nutrient removal (BNR) type MWTP in Saskatoon, SK, Canada. This MWTP experiences seasonal temperatures from −40 °C to 30 °C with the resultant wastewater temperatures considered herein of 13 °C and 17 °C being chosen based on monitoring data for winter and summer, respectively. Laboratory-scale reactors simulating anaerobic, anoxic, aerobic, and settling treatment processes were used to monitor gas EREs using wastewater samples taken from the analogous MWTP processes during the winter and summer seasons. Results indicated that the overall winter EREs for CO2, CH4, and N2O were 45,129 kg CO2/d, 21.9 kg CH4/d, and 3.20 kg N2O/d, respectively, while the H2S EREs were insignificant. The higher temperature for the summer samples resulted in increased EREs for CH4, N2O, and H2S EREs of 33.0 kg CH4/d, 3.87 kg N2O/d, and 2.29 kg H2S/d, respectively. However, the CO2 EREs were reduced to 37,794 kg CO2/d. Overall, the aerobic reactor was the dominant source of the GHG emissions for both seasons. In addition, studied changes in the aerobic reactor aeration rates (in reactor) and BNR treatment configurations (from site) further impacted the EREs.

Indoor use of essential oil-based cleaning products: Emission rate and indoor air quality impact assessment based on a realistic application methodology

Cleaning is performed to increase hygiene, esthetics and material preservation. Despite its benefits, cleaning also poses risks, potentially contributing to nearly 20% of indoor pollution. As indoor air quality has become a major human health concern, “natural-scented” cleaning products, formulated with essential oils, have become market leaders among household products. However, these consumer products have benefitted from skillful marketing strategies based on the ambiguity of the words “green” and “natural”. The characterization of the emission processes studied through 1-m3 chamber experiments under a realistic scenario suggests variable maximum total terpene concentrations from 150 to 300 ppb. The estimated emission rate profiles confirm that the liquid-to-gas transfer of terpenes is driven by (i) the formulation of the product matrix inducing specific chemical affinities, (ii) the liquid content of individual terpenes, and (iii) the intrinsic volatility of terpenes. A unique formaldehyde emission kinetics profile was observed, suggesting the presence of a unique emission source: formaldehyde-releasers. Consequently, the use of essential-oil-based cleaning products might generate a long-term increase in the indoor formaldehyde concentration, and the maximum levels might be sustained for several hours after cleaning. Thus, essential-oil-based cleaners should be seriously considered as versatile and significant sources of fragrance molecules and formaldehyde.

First In-Situ Measurements of Plume Chemistry at Mount Garet Volcano, Island of Gaua (Vanuatu)

Recent volcanic gas compilations have urged the need to expand in-situ plume measurements to poorly studied, remote volcanic regions. Despite being recognized as one of the main volcanic epicenters on the planet, the Vanuatu arc remains poorly characterized for its subaerial emissions and their chemical imprints. Here, we report on the first plume chemistry data for Mount Garet, on the island of Gaua, one of the few persistent volatile emitters along the Vanuatu arc. Data were collected with a multi-component gas analyzer system (multi-GAS) during a field campaign in December 2018. The average volcanic gas chemistry is characterized by mean molar CO2/SO2, H2O/SO2, H2S/SO2 and H2/SO2 ratios of 0.87, 47.2, 0.13 and 0.01, respectively. Molar proportions in the gas plume are estimated at 95.9 ± 11.6, 1.8 ± 0.5, 2.0 ± 0.01, 0.26 ± 0.02 and 0.06 ± 0.01, for H2O, CO2, SO2, H2S and H2. Using the satellite-based 10-year (2005–2015) averaged SO2 flux of ~434 t d−1 for Mt. Garet, we estimate a total volatile output of about 6482 t d−1 (CO2 ~259 t d−1; H2O ~5758 t d−1; H2S ~30 t d−1; H2 ~0.5 t d−1). This may be representative of a quiescent, yet persistent degassing period at Mt. Garet; whilst, as indicated by SO2 flux reports for the 2009–2010 unrest, emissions can be much higher during eruptive episodes. Our estimated emission rates and gas composition for Mount Garet provide insightful information on volcanic gas signatures in the northernmost part of the Vanuatu Arc Segment. The apparent CO2-poor signature of high-temperature plume degassing at Mount Garet raises questions on the nature of sediments being subducted in this region of the arc and the possible role of the slab as the source of subaerial CO2. In order to better address the dynamics of along-arc volatile recycling, more volcanic gas surveys are needed focusing on northern Vanuatu volcanoes.

Modelling air change rate of naturally ventilated dairy buildings using response surface methodology and numerical simulation

The air change rate (ACR) of naturally ventilated dairy buildings (NVDBs) plays an important part in the design and control of the ventilation system, as well as in the estimation of the gaseous emission rate. The objectives of this research were to model the ACR based on a quantitative investigation of the relationship between the ACR and its potential influencing factors, including the opening ratio (r), the building length to width ratio (α), the wind speed (U), and the wind direction (θ). The investigations were performed using the response surface methodology integrated with the Box-Behnken design and Computational Fluid Dynamics (CFD) simulations. Three response surface models of the ACR of NVDBs were established for three opening ratio ranges of 5%–42.5%, 42.5%–80%, and 5%–80%, respectively. It was found that the selection of the opening ratio range had almost no effect on the developed response surface models. The results showed that the ACR of NVDBs was not influenced by α, but was significantly affected by r, U, θ, and interaction effects between every two of the three factors. The highest ACR was 6.7 s−1, 6.0 s−1, and 4.0 s−1 when θ, U, and r was at their respective medium value while the rest parameters were at the highest values, indicating that the r played an important role in the value of ACR. It was concluded that in the prediction of the ACR of a building, the influences of both individual and interactional effects of θ, U, and r should be considered.

Prediction of Odor Concentration Emitted from Wastewater Treatment Plant Using an Artificial Neural Network (ANN)

The odor emitted from a wastewater treatment plant (WWTP) is an important environmental problem. An estimation of odor emission rate is difficult to detect and quantify. To address this, various approaches including the development of emission factors and measurement using a closed chamber have been employed. However, the evaluation of odor emission involves huge manpower, time, and cost. An artificial neural network (ANN) is recognized as an efficient method to find correlations between nonlinear data and prediction of future data based on these correlations. Due to its usefulness, ANN is used to solve complicated problems in various disciplines of sciences and engineering. In this study, a method to predict the odor concentration in a WWTP using ANN was developed. The odor concentration emitted from a WWTP was predicted by the ANN based on water quality data such as biological oxygen demand, dissolved oxygen, and pH. The water quality and odor concentration data from the WWTP were measured seasonally in spring, summer, and autumn and these were used as input variations to the ANN model. The odor predicted by the ANN model was compared with the measured data and the prediction accuracy was estimated. Suggestions for improving prediction accuracy are presented.

Measurement of ozone deposition velocity onto human surfaces of Chinese residents and estimation of corresponding production of oxidation products

China has been experiencing a sharp increase in outdoor ozone concentration. The deposition velocity of ozone onto human surfaces strongly affects the indoor ozone concentration, and the production of oxidation products. In this study, we measured the deposition velocity of ozone onto human surfaces in six Chinese residential rooms, each housing 1–3 occupants. In addition, we estimated the corresponding emission rate of oxidation products. The measured mean value of the deposition velocity is 14.8 (±10.1) m/h, which is close to those reported in earlier studies in western countries. We also find that the ozone deposition velocity onto human surfaces is positively correlated to the weighted duration from the last laundry and bath/facial-cleansing, and it is negatively correlated to the relative humidity of the indoor air. The estimated emission rates of 6-MHO, 4-OPA, acetone, geranyl acetone, 1,4-butanedial decanal, and all gas-phase products are 7.7 × 1016, 6.7 × 1016, 9.4 × 1016, 9.7 × 1015, 1.3 × 1016, 3.4 × 1016, and 4.8 × 1017 molecule per person h−1, respectively, with an indoor ozone concentration of 5 ppb at steady state. The measured deposition velocity of ozone onto human surfaces of Chinese residents, and the emission rates of the corresponding oxidation products may be applied for the estimation of human exposure to ozone and its oxidation products in China.