Comparative analysis for the impacts of VOC subgroups and atmospheric oxidation capacity on O3 based on different observation-based methods at a suburban site in the North China Plain.

The persistent O3 pollution in the Beijing-Tianjin-Hebei (BTH) region remains unresolved, largely due to limited comprehension of O3-precursor relationship and photochemistry drivers. In this work, intraday O3 sensitivity evolution from VOC-limited (volatile organic compound) regime in the forenoon to transition regime in the late afternoon was inferred by relative incremental reactivity (RIR) in summer 2019 at Xianghe, a suburban site in BTH region, suggesting that VOC-focused control policy could combine with stringent afternoon NOx control. Then detailed impacts of VOC subgroups on O3 formation were further comprehensively quantified by parametric OH reactivity (KOH), O3 formation potential (OFP), as well as RIR weighted value and O3 formation path tracing (OFPT) approach based on photochemical box model. O3 episode days corresponded to stronger O3 formation, depicted by higher KOH (10.4 s-1), OFP (331.7 µg m-3), RIR weighted value (1.2), and F(O3)-OFPT (15.5 ppbv h-1). High proportions of isoprene and OVOCs (oxygenated VOCs) to the total KOH and the OFPT method were demonstrated whereas results of OFP and RIR-weighted presented extra great impacts of aromatics on O3 formation. The OFPT approach captured the process that has already happened and included final O3 response to the original VOC, thus reliable for replicating VOC impacts. The comparison results of the four methods showed similarities when utilizing KOH and OFPT methods, which reveals that the potential applicability of simple KOH for contingency VOC control and more complex OFPT method for detailed VOC- and source-oriented control during policy-making. To investigate propulsion of VOC-involved O3 photochemistry, atmospheric oxidation capacity (AOC) was quantified by two atmospheric oxidation indexes (AOI). Both AOIp_G (7.0 × 107 molec cm-3 s-1, potential AOC calculated by oxidation reaction rates) and AOIe_G (8.5 µmol m-3, estimated AOC given redox electron transfer for oxidation products) were stronger on O3 episode days, indicating that AOC promoted the radical cycling initiated from VOC oxidation and subsequent O3 production. Result-oriented AOIe_G reasonably characterized actual AOC inferred by good linear correlation between AOIe_G and O3 concentrations compared to process-oriented AOIp_G. Therefore, with continuous NOx abatement, AOIe_G should be considered to represent actual AOC, also O3-inducing ability.

Double-Edged Role of VOCs Reduction in Nitrate Formation: Insights from Observations during the China International Import Expo 2018.

Aerosol nitrate (NO3-) constitutes a significant component of fine particles in China. Prioritizing the control of volatile organic compounds (VOCs) is a crucial step toward achieving clean air, yet its impact on NO3- pollution remains inadequately understood. Here, we examined the role of VOCs in NO3- formation by combining comprehensive field measurements conducted during the China International Import Expo (CIIE) in Shanghai (from 10 October to 22 November 2018) and multiphase chemical modeling. Despite a decline in primary pollutants during the CIIE, NO3- levels increased compared to pre-CIIE and post-CIIE─NO3- concentrations decreased in the daytime (by -10 and -26%) while increasing in the nighttime (by 8 and 30%). Analysis of the observations and backward trajectory indicates that the diurnal variation in NO3- was mainly attributed to local chemistry rather than meteorological conditions. Decreasing VOCs lowered the daytime NO3- production by reducing the hydroxyl radical level, whereas the greater VOCs reduction at night than that in the daytime increased the nitrate radical level, thereby promoting the nocturnal NO3- production. These results reveal the double-edged role of VOCs in NO3- formation, underscoring the need for transferring large VOC-emitting enterprises from the daytime to the nighttime, which should be considered in formulating corresponding policies.

Decoupling of Estuarine Hypoxia and Acidification as Revealed by Historical Water Quality Data.

Hypoxia and acidification are commonly coupled in eutrophic aquatic environments because aerobic respiration is usually dominant in bottom waters and can lower dissolved oxygen (DO) and pH simultaneously. However, the degree of coupling, which can be weakened by non-aerobic respiration and CaCO3 cycling, has not been adequately assessed. In this study, we applied a box model to 20 years of water quality monitoring data to explore the relationship between hypoxia and acidification along the mainstem of Chesapeake Bay. In the early summer, dissolved inorganic carbon (DIC) production in mid-bay bottom waters was dominated by aerobic respiration, contributing to DO and pH declines. In contrast, late-summer DIC production was higher than that expected from aerobic respiration, suggesting potential buffering processes, such as calcium carbonate dissolution, which would elevate pH in hypoxic waters. These findings are consistent with contrasting seasonal relationships between riverine nitrogen (N) loads and hypoxic and acidified volumes. The N loads were associated with increased hypoxic and acidified volumes in June, but only increased hypoxic volumes in August, when acidified volume declines instead. Our study reveals that the magnitude of this decoupling varies interannually with watershed nutrient inputs, which has implications for the management of co-stressors in estuarine systems.

Unraveling the O3-NOX-VOCs relationships induced by anomalous ozone in industrial regions during COVID-19 in Shanghai.

The COVID-19 pandemic promoted strict restrictions to human activities in China, which led to an unexpected increase in ozone (O3) regarding to nitrogen oxides (NOx) and volatile organic compounds (VOCs) co-abatement in urban China. However, providing a quantitative assessment of the photochemistry that leads to O3 increase is still challenging. Here, we evaluated changes in O3 arising from photochemical production with precursors (NOX and VOCS) in industrial regions in Shanghai during the COVID-19 lockdowns by using machine learning models and box models. The changes of air pollutants (O3, NOX, VOCs) during the COVID-19 lockdowns were analyzed by deweathering and detrending machine learning models with regard to meteorological and emission effects. After accounting for effects of meteorological variability, we find increase in O3 concentration (49.5%). Except for meteorological effects, model results of detrending the business-as-usual changes indicate much smaller reduction (-0.6%), highlighting the O3 increase attributable to complex photochemistry mechanism and the upward trends of O3 due to clear air policy in Shanghai. We then used box models to assess the photochemistry mechanism and identify key factors that control O3 production during lockdowns. It was found that empirical evidence for a link between efficient radical propagation and the optimized O3 production efficiency of NOX under the VOC-limited conditions. Simulations with box models also indicate that priority should be given to controlling industrial emissions and vehicle exhaust while the VOCs and NOX should be managed at a proper ratio in order to control O3 in winter. While lockdown is not a condition that could ever be continued indefinitely, findings of this study offer theoretical support for formulating refined O3 management in industrial regions in Shanghai, especially in winter.

A review of gas-phase chemical mechanisms commonly used in atmospheric chemistry modelling.

The atmospheric chemical mechanism is an essential component of airshed models used for investigating the chemical behaviors and impacts of species. Since the first tropospheric chemical mechanism was proposed in the 1960s, various mechanisms including Master Chemical Mechanism (MCM), Carbon Bond Mechanism (CBM), Statewide Air Pollution Research Center (SAPRC) and Regional Atmospheric Chemistry Mechanism (RACM) have been developed for different research purposes. This work summarizes the development and applications of these mechanisms, introduces their compositions and lumping methods, and compares the ways the mechanisms treat radicals with box model simulations. CBM can reproduce urban pollution events with relatively low cost compared to SAPRC and RACM, whereas the chemical behaviors of radicals and the photochemical production of ozone are described in detail in RACM. The photolysis rates of some oxygenated compounds are low in SAPRC07, which may result in underestimation of radical levels. As an explicit chemical mechanism, MCM describes the chemical processes of primary pollutants and their oxidation products in detail. MCM can be used to investigate certain chemical processes; however, due to its large size, it is rarely used in regional model simulations. A box model case study showed that the chemical behavior of OH and HO2 radicals and the production of ozone were well described by all mechanisms. CBM and SAPRC underestimated the radical levels for different chemical treatments, leading to low ozone production values in both cases. MCM and RACM are widely used in box model studies, while CBM and SAPRC are often selected in regional simulations.

Gray-box Soft Sensor for Water Content Monitoring in Fluidized Bed Granulation.

Soft sensors are powerful tools for the implementation of process analytical technology (PAT). They are categorized into white-box (first-principle), black-box (statistical), and gray-box models. Gray-box models integrate white-box and black-box models to address each drawback, i.e., prediction accuracy and intuitiveness. Although they have been applied to various industrial processes, their applicability to water content monitoring in fluidized bed granulation has not been reported. In this study, we evaluated three types of gray-box models, i.e., parallel, serial, and combined gray-box models, in terms of prediction accuracy using real operating data on a commercial scale with two formulations. The gray-box models were constructed by integrating the heat and mass balance model (white-box model) and locally weighted partial least squares regression (LW-PLSR) model (black-box model). LW-PLSR was utilized to cope with collinearity and nonlinearity. In the serial gray-box models, LW-PLSR models adjusted the fitting parameters of the white-box model depending on the process parameters for each query. In the parallel gray-box or combined gray-box models, LW-PLSR models compensated for the output error of the white-box or serial gray-box models, respectively. The results demonstrated that all three types of gray-box models improved the prediction accuracy of the white-box models regardless of the formulation. Besides, we proposed the assessment method based on Hotelling's T2 and Q residual for gray-box models using LW-PLSR, which contributes decision support to select gray-box or white-box model. The accurate and descriptive gray-box models are expected to enhance process understanding and precise quality control in fluidized bed granulation.

Worsening ozone air pollution with reduced NOx and VOCs in the Pearl River Delta region in autumn 2019: Implications for national control policy in China.

Ozone (O3) pollution has emerged as a major air quality issue in China. Here we emphasize the great challenges in controlling O3 pollution by analyzing the recent experience of the Pearl River Delta (PRD) in southern China in reducing the autumn O3 peaks. Despite significant reductions in the concentration of O3 precursors, i.e., nitrogen oxides (NOx) and volatile organic compounds (VOCs), regional O3 pollution in the PRD was largely worse in autumn 2019 than in autumn 2018. We found that the supra-regional and regional background concentrations of O3 increased significantly in the PRD in autumn 2019 due to increased concentrations of O3 in the vast surrounding areas. We also observed slight increases in the concentrations of PRD-regionally and Guangzhou-locally produced O3. A chemical box-model analysis confirmed a slight increase in the in-situ production of O3 and revealed that increased biogenic VOCs (BVOCs) and decreased NOx levels negated the effect of significant decrease in the anthropogenic VOCs. Taken together, these aspects exacerbated O3 pollution in the PRD region in autumn 2019 relative to autumn 2018. The findings from this study highlight the strong interactions of O3 pollution over multiple regions and the need for collaborative inter-regional efforts to control O3 pollution. The experience of PRD also underlines the key role of BVOCs and the importance of science-based strategies to decrease VOCs and NOx.

Inter-comparisons of VOC oxidation mechanisms based on box model: A focus on OH reactivity.

Volatile organic compounds (VOCs) oxidation processes play a very important role in atmospheric chemistry, and the chemical reactions are expressed in various manners in chemical mechanisms. To gain an improved understanding of VOCs evolution during oxidation processes and evaluate the discrepancies of VOCs oxidation schemes among different mechanisms, we used the total VOC reactivity as a diagnostic and evaluated tool to explore the differences for six widely used chemical mechanisms. We compared the total VOC reactivity evolution under high-NOx conditions for several sets of precursors, including n-pentane, toluene, ethene, isoprene and a mixture of 57 Photochemical Assessment Monitoring Stations (PAMS) species in a 0-D photochemical box model. Inter-comparison of total VOC reactivity of individual precursor simulations showed discrepancies to different extent of the oxidation schemes among the studied mechanisms, which are mainly attributed to the different lumping approaches for organic species. The PAMS simulation showed smaller discrepancy than individual precursor cases in terms of total VOC reactivity. SAPRC07 and RACM2 performances are found to better match the MCM for simulation of total VOC reactivity. Evidences suggest that the performance in simulating secondary organic products, OH concentrations and NOx concentrations are related to the OH reactivity discrepancies among various chemical mechanisms. Information in this study can be used in selection of chemical mechanisms to better model OH reactivity in different environments. The results in this study also provide directions to further improve the ability in modelling total VOC reactivity with the chemical mechanisms.

Ozone episodes during and after the 2018 Chinese National Day holidays in Guangzhou: Implications for the control of precursor VOCs.

The impact of reducing industrial emissions of volatile organic compounds (VOCs) on ozone (O3) pollution is of wide concern particularly in highly industrialized megacities. In this study, O3, nitrogen oxides (NOx) and VOCs were measured at an urban site in the Pearl River Delta region during the 2018 Chinese National Day Holidays and two after-holiday periods (one with ozone pollution and another without). O3 pollution occurred throughout the 7-day holidays even industrial emissions of VOCs were passively reduced due to temporary factory shutdowns, and the toluene to benzene ratios dropped from ∼10 during non-holidays to ∼5 during the holidays. Box model (AtChem2-MCM) simulations with the input of observation data revealed that O3 formation was all VOC-limited, and alkenes had the highest relative incremental reactivity (RIR) during the holiday and non-holiday O3 episodes while aromatics had the highest RIR during the non-pollution period. Box model also demonstrated that even aromatics decreased proportionally to levels with near-zero contributions of industrial aromatic solvents, O3 concentrations would only decrease by less than 20% during the holiday and non-holiday O3 episodes and ozone pollution in the periods could not be eliminated. The results imply that controlling emissions of industrial aromatic solvents might be not enough to eliminate O3 pollution in the region, and more attention should be paid to anthropogenic reactive alkenes. Isoprene and formaldehyde were among the top 3 species by RIRs in all the three pollution and non-pollution periods, suggesting substantial contribution to O3 formation from biogenic VOCs.

Characteristics of ambient volatile organic compounds during spring O3 pollution episode in Chengdu, China.

Surface ozone (O3) has become a critical pollutant impeding air quality improvement in many Chinese megacities. Chengdu is a megacity located in Sichuan Basin in southwest China, where O3 pollution occurs frequently in both spring and summer. In order to understand the elevated O3 during spring in Chengdu, we conducted sampling campaign at three sites during O3 pollution episodes in April. Volatile organic compounds (VOCs) compositions at each site were similar, and oxygenated VOCs (OVOCs) concentrations accounted for the highest proportion (35%-45%), followed by alkanes, alkens (including acetylene), halohydrocarbons, and aromatics. The sensitivity of O3 to its precursors was analyzed using an observation based box model. The relative incremental reactivity of OVOCs was larger than other precursors, suggesting that they also played the dominant role in O3 formation. Furthermore, the positive matrix factorization model was used to identify the dominant emission sources and to evaluate their contribution to VOCs in the city. The main sources of VOCs in spring were from combustion (27.75%), industrial manufacturing (24.17%), vehicle exhaust (20.35%), and solvent utilization (18.35%). Discussions on VOCs and NOx reduction schemes suggested that Chengdu was typical in the VOC-limited regime, and VOC emission reduction would help to prevent and control O3. The analysis of emission reduction scenarios based on VOCs sources showed that the emission reduction ratio of VOCs to NO2 needs to reach more than 3 in order to achieve O3 prevention. Emission reduction from vehicular exhaust source and solvent utilization source may be more effective.

From SIR to SEAIRD: A novel data-driven modeling approach based on the Grey-box System Theory to predict the dynamics of COVID-19.

Common compartmental modeling for COVID-19 is based on a priori knowledge and numerous assumptions. Additionally, they do not systematically incorporate asymptomatic cases. Our study aimed at providing a framework for data-driven approaches, by leveraging the strengths of the grey-box system theory or grey-box identification, known for its robustness in problem solving under partial, incomplete, or uncertain data. Empirical data on confirmed cases and deaths, extracted from an open source repository were used to develop the SEAIRD compartment model. Adjustments were made to fit current knowledge on the COVID-19 behavior. The model was implemented and solved using an Ordinary Differential Equation solver and an optimization tool. A cross-validation technique was applied, and the coefficient of determination R 2 was computed in order to evaluate the goodness-of-fit of the model. Key epidemiological parameters were finally estimated and we provided the rationale for the construction of SEAIRD model. When applied to Brazil's cases, SEAIRD produced an excellent agreement to the data, with an R 2 ≥ 90%. The probability of COVID-19 transmission was generally high (≥ 95%). On the basis of a 20-day modeling data, the incidence rate of COVID-19 was as low as 3 infected cases per 100,000 exposed persons in Brazil and France. Within the same time frame, the fatality rate of COVID-19 was the highest in France (16.4%) followed by Brazil (6.9%), and the lowest in Russia (≤ 1%). SEAIRD represents an asset for modeling infectious diseases in their dynamical stable phase, especially for new viruses when pathophysiology knowledge is very limited. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10489-021-02379-2.

Novel Application of Machine Learning Algorithms and Model-Agnostic Methods to Identify Factors Influencing Childhood Blood Lead Levels.

Blood lead (Pb) poisoning remains a global concern, particularly for children in their early developmental years. Broken Hill is Australia's oldest operating silver-zinc-lead mine. In this study, we utilized recent advances in machine learning to assess multiple algorithms and identify the most optimal model for predicting childhood blood Pb levels (BLL) using Broken Hill children's (<5 years of age) data (n = 23,749) from 1991 to 2015, combined with demographic, socio-economic, and environmental influencing factors. We applied model-agnostic methods to interpret the most optimal model, investigating different environmental and human factors influencing childhood BLL. Algorithm assessment showed that stacked ensemble, a method for automatically and optimally combining multiple prediction algorithms, enhanced predictive performance by 1.1% with respect to mean absolute error (p < 0.01) and 2.6% for root-mean-squared error (p < 0.01) compared to the best performing constituent algorithm (random forest). By interpreting the model, the following information was acquired: children had higher BLL if they resided within 1.0 km to the central mine area or 1.37 km to the railroad; year of testing had the greatest interactive strength with all other factors; BLL increased faster in Aboriginal than in non-Aboriginal children at 9-10 and 12-18 months of age. This "stacked ensemble + model-agnostic interpretation" framework achieved both prediction accuracy and model interpretability, identifying previously unconnected variables associated with elevated childhood BLL, offering a marked advantage over previous works. Thus, this approach has a clear value and potential for application to other environmental health issues.

Modeling occupational exposure to solvent vapors using the Two-Zone (near-field/far-field) model: a literature review.

The Two-Zone model is used in occupational hygiene to predict both near-field and far-field airborne contaminant concentrations. A literature review was carried out on 21 scientific publications in which the Two-Zone model was used to assess occupational exposure to solvent vapors. Data on exposure scenarios, solvents, generation/emission rates, near- and far-field parameters, and model performance were collected and analyzed. Over the 24 exposure scenarios identified, 18 were evaluated under controlled conditions, 5 under normal workplace activities, and 1 was reported based on literature data. The scenarios involved a variety of tasks which consisted, mostly, of cleaning metal parts, spraying solvents onto surfaces, spilling liquids, and filling containers with volatile substances. Twenty-eight different solvents were modeled and the most commonly tested were benzene, toluene, and acetone. Emission rates were considered constant in 16 scenarios, exponentially decreasing in 6 scenarios, and intermittent in 2 scenarios. Four-hundred-and-forty-six (446) predicted-to-measured concentration ratios were calculated across the 21 studies; 441 were obtained in controlled conditions, 4 under normal workplace activities, and 1 was calculated based on the literature data. For controlled studies, the Two-Zone model predictive performance was within a factor of 0.3-3.7 times the measured concentrations with 93% of the values between 0.5 and 2. The model overestimated the measured concentrations in 63% of the evaluations. The median predicted concentration for the near-field was 1.38 vs. 1.02 for the far-field. Results suggest that the model might be a useful tool for predicting occupational exposure to vapors of solvents by providing a conservative approach. Harmonization in model testing strategies and data presentation is needed in future studies to improve the assessment of the predictability of the Two-Zone model. Moreover, this review has provided a database of exposure scenarios, input parameter values, and model predictive performances which can be useful to occupational hygienists in their future modeling activities.

Innovations in savings behaviour and agricultural business support for women: The Ghanaian 'Money-Box'.

In Ghana, like many African countries, women are financially disadvantaged compared to men, which has encouraged women to form credit associations. This paper examined the "Pagwuni" Women's Group (PWG) in the Northern region of Ghana, where over 93 percent of members are smallholder farmers. Women make regular membership and social fund contributions with records kept in a secured 'money-box', opened at an agreed time. Members receive their money according to the amount contributed while the 'social fund' is for purchasing farm inputs. This study analysed the effect of the Pagwuni Women Group (PWG) financing activities on the financial needs of the group and their households' economic welfare. The study also examined the kind of innovation that this PWG adopts that differs from the traditional Village Savings and Loans Association (VSLA). In investigating the sustainability of this 'money-box' model, data was sourced from 150 participants and the group administrators using a mixed research method. Three significant findings were uncovered. First, 93.3 percent of the respondents are peasant farmers. Second, 86.7percent indicated that this money-box arrangement is their only form of savings. Hence, since PWG is a woman-only group, it offers a good opportunity to understand the interconnections between gender and the economic welfare, enhancing the potential benefits of such group savings associations. Third, access to the social fund component of money-box helped the female participants to acquire the equipment and services needed to modernise their farming activities. Policy recommendations to link such rural money box initiatives with formal financial institutions to help channel funds to small-scale female farmers are discussed.

Outwelling of total alkalinity and dissolved inorganic carbon from the Hooghly River to the adjacent coastal Bay of Bengal.

The seasonal variability of the lateral flux of total alkalinity (TAlk) and dissolved inorganic carbon (DIC) of the tropical Hooghly estuary is analyzed in this work. In situ observations of water temperature, salinity, dissolved oxygen, TAlk, and pH were measured in four different stations of the Hooghly estuary. It was measured once every month during 2015-2016, and subsequently, DIC was estimated. A carbon budget was constructed to quantify carbon flows through the freshwater-marine continuum of the Hooghly estuary, and plausible impacts on the adjacent coastal ocean, the northern Bay of Bengal, were examined. The biogeochemical mass balance box model was used to compute the seasonal flow of carbon flux, and subsequently, the annual budgeting of lateral fluxes of TAlk and DIC to the adjacent coastal ocean was carried out. The net annual TAlk and DIC flux from the Hooghly estuary to the adjacent coastal ocean were 4.45 ± 1.90 × 1011 mol and 4.59 ± 1.70 × 1011 mol, respectively. The net annual DIC flux of the Hooghly estuary is about 30 to 60 times higher than surface area integrated air-water CO2 flux, which is an indication of promoting acidification in the adjacent coastal ocean. The present study indicates that the lateral DIC flux has increased substantially in the Hooghly estuary during the last two decades. The increase in inorganic carbon load in the Hooghly estuary due to the enhanced discharge of inorganic and organic matter load in the upper reaches of the estuary led to this increase in lateral DIC flux. The results strongly establish the need of having such regional studies for better understanding the estuarine carbon dynamics, and its role in controlling the adjacent coastal ocean dynamics.

Comparing regional stove-usage patterns and using those patterns to model indoor air quality impacts.

Monitoring improved cookstove adoption and usage in developing countries can help anticipate potential health and environmental benefits that may result from household energy interventions. This study explores stove-usage monitor (SUM)-derived usage data from field studies in China (52 stoves, 1422 monitoring days), Honduras (270 stoves, 630 monitoring days), India (19 stoves, 565 monitoring days), and Uganda (38 stoves, 1007 monitoring days). Traditional stove usage was found to be generally similar among four seemingly disparate countries in terms of cooking habits, with average usage of between 171 and 257 minutes per day for the most-used stoves. In Honduras, where survey-based usage data were also collected, there was only modest agreement between sensor data and self-reported user data. For Indian homes, we combined stove-usage data with a single-zone Monte Carlo box model to estimate kitchen-level PM2.5 and CO concentrations under various scenarios of cleaner cookstove adoption. We defined clean cookstove performance based on the International Standards Organization (ISO) voluntary guidelines. Model results showed that even with 75% displacement of traditional stoves with the cleanest available stove (ISO tier-5), World Health Organization 24 hours PM2.5 standards were exceeded in 96.4% of model runs, underscoring the importance of full displacement.

Estimating the remaining atmospheric environmental capacity using a single-box model in a high pollution risk suburb of Chengdu, China.

The atmospheric pollution has been the public attention in recent years. In order to better coordinate economic development and atmospheric environmental management, China introduced the concept of atmospheric environmental capacity (AEC). The remaining atmospheric environmental capacity (RAEC) calculated by existing atmospheric pollution sources and AEC is an important basis for regional development and environmental protection. The RAEC of the high-pollution risk suburb of Chengdu in 2015 was estimated by the single-box model and analyzed on multiple time scales. The results show that the RAEC of SO2 and NO2 in this region is 3299 t/a and 2849 t/a, respectively under the annual time scale. However, in the daily time scale, the RAEC of NO2 is negative for 3 days, that is, there are 3 days with serious air pollution. Therefore, it is not appropriate to plan the industrial area only by relying on annual RAEC. Especially, RAEC displays inter-seasonal and monthly variability. On the one hand, in plain areas with low wind speed and little change in wind direction, achieving the prediction of atmospheric mixing layer height could give early warning of atmospheric pollution events. On the other hand, different management measures are taken on different time scales. On a long timescale, the regional energy structure should be optimized. On seasonal and monthly time scales, the production plans should be adapted to RAEC. On the daily time scale, it mainly deals with the serious atmospheric pollution accident timely.

An experimental application of laser-scattering sensor to estimate the traffic-induced PM2.5 in Beijing.

Traffic-induced air pollutant emissions are currently rising rapidly. However, measurement of the roadside environment and calculation of the emission factors for traffic-induced PM2.5 are restricted to certain locations and periods due to the limitations of conventional air monitoring techniques. This paper introduces a portable sensor package with a laser light-scattering PM2.5 sensor and an electrochemical CO sensor to measure roadside PM2.5 and CO concentrations. The low-cost sensor package underwent local calibration using reference instruments at the Chinese Research Academy of Environmental Sciences (CRAES). The results showed a high level of correlation (r in the range of 0.94-0.95 and 0.81-0.83 for PM2.5 and CO, respectively) between measurements using the sensor packages and those measured by the reference equipment. The study found that the low-cost sensor packages were able to deliver reliable measurements of PM2.5 and CO concentrations. Four low-cost sensor packages were deployed along a short section of an expressway to measure roadside PM2.5 and CO concentrations. The directly measured concentrations were firstly calibrated with the temperature and humidity. The corrected PM2.5 concentrations from each side of the road were different, while the corrected CO concentrations were similar on both sides of the road. Therefore, only the PM2.5 measurements were applied in this study's box model. The assumption of perfect mixing in order for the box model to be applied was shown by the results to be valid to some extent. The PM2.5 emission factors for opposite sides of the road should be calculated separately based on the direction of traffic flow. The PM2.5 emission factors calculated in this study were variable, being impacted by traffic conditions and meteorological conditions. The paper presents a method for obtaining PM2.5 emission factors based on a box model. This method is a promising way of monitoring air pollution in the roadside environment.

Indoor dispersion of airborne nano and fine particles: Main factors affecting spatial and temporal distribution in the frame of exposure modeling.

A particle exposure experiment inside a large climate-controlled chamber was conducted. Data on spatial and temporal distribution of nanoscale and fine aerosols in the range of mobility diameters 8-600 nm were collected with high resolution, for sodium chloride, fluorescein sodium, and silica particles. Exposure scenarios studied included constant and intermittent source emissions, different aggregation conditions, high (10 h-1 ) and low (3.5 h-1 ) air exchange rates (AERs) corresponding to chamber Reynolds number, respectively, equal to 1 × 105 and 3 × 104 . Results are presented and analyzed to highlight the main determinants of exposure and to determine whether the assumptions underlying two-box models hold under various scenarios. The main determinants of exposure found were the source generation rate and the ventilation rate. The effect of particles nature was indiscernible, and the decrease of airborne total number concentrations attributable to surface deposition was estimated lower than 2% when the source was active. A near-field/far-field structure of aerosol concentration was always observed for the AER = 10 h-1 but for AER = 3.5 h-1 , a single-field structure was found. The particle size distribution was always homogeneous in space but a general shift of particle diameter (-8% to +16%) was observed between scenarios in correlation with the AER and with the source position, presumably largely attributable to aggregation.

A methodology for the assessment of inhalation exposure to aluminium from antiperspirant sprays.

Inhalative exposure can occur accidentally when using cosmetic spray products. Usually, a tiered approach is applied for exposure assessment, starting with rather conservative, simplistic calculation models that may be improved with measured data and more refined modelling. Here we report on an advanced methodology to mimic in-use conditions for antiperspirant spray products to provide a more accurate estimate of the amount of aluminium possibly inhaled and taken up systemically, thus contributing to the overall body burden. Four typical products were sprayed onto a skin surrogate in defined rooms. For aluminium, size-related aerosol release fractions, i.e. inhalable, thoracic and respirable, were determined by a mass balance method taking droplet maturation into account. These data were included into a simple two-box exposure model, allowing calculation of the inhaled aluminium dose over 12 min. Systemic exposure doses were calculated for exposure of the deep lung and the upper respiratory tract using the Multiple Path Particle Deposition Model (MPPD) model. The total systemically available dose of aluminium was in all cases found to be less than 0.5 µg per application. With this study it could be demonstrated that refinement of the input data of the two-box exposure model with measured data of released airborne aluminium is a valuable approach to analyse the contribution of antiperspirant spray inhalation to total aluminium exposure as part of the overall risk assessment. We suggest the methodology which can also be applied to other exposure modelling approaches for spray products, and further is adapted to other similar use scenarios.