Pollutant Concentration Fields Research Articles

SIMBAD: a simplified emission-concentration model for a computationally efficient assessment of energy policies impact on air quality

The reconstruction of airborne pollutant concentration fields based on emission reduction scenarios is a complex task. Simulations with chemistry and transport models (CTM) are computationally expensive and not suited for iterative optimisation that could require the evaluation of a great number of scenarios. To address this, data-driven surrogate models have been used to approximate the relationship between emission and ambient air concentrations. In this study, a different approach is presented. SIMBAD is a simplified model based on CAMx (Comprehensive Air Quality Model with Extensions) and the Direct Decoupled Method (DDM) algorithm that estimates concentration changes due to variations in emission fields. SIMBAD was validated by comparing PM10 and NO2 concentrations with CAMx simulations implementing the same emission variations. The testing scenarios involved different emission reductions and precursor species to assess SIMBAD’s performance in both simple and complex cases. The model’s performance in reproducing the non-linear nature of atmospheric processes was satisfactory, showing an average Root Mean Square Error (RMSE) always lower than 0.2 μg/m3 and a normalised bias below 2%. Slightly lower accuracy was found in more complex scenarios involving multiple pollutants and sectors modified simultaneously. Overall, SIMBAD proved to be an efficient and accurate tool for evaluating the impacts of energy policies on air quality, providing valuable insights for policymakers and researchers alike.

High-spatiotemporal-resolution mapping of PM2.5 traffic source impacts integrating machine learning and source-specific multipollutant indicator

Traffic sources are a major contributor to fine particulate matter (PM2.5) pollution, with their emissions and diffusion exhibiting complex spatiotemporal patterns. Receptor models have limitations in estimating high-resolution source contributions due to insufficient observation networks of PM2.5 compositions. This study developed a source apportionment method that integrates machine learning and emission-based integrated mobile source indicator (IMSI) to rapidly and accurately estimate PM2.5 traffic source impacts with high spatiotemporal resolution in the Beijing-Tianjin-Hebei region. Firstly, we utilized multisource data and developed various machine learning models to optimize the traffic-related pollutant concentration fields simulated by a chemical transport model. Results demonstrated that the Extreme Gradient Boosting (XGBoost) model exhibited excellent prediction accuracy of nitrogen oxide (NO2), carbon oxide (CO), and elemental carbon (EC), with the cross-validated R values increasing to 0.87–0.92 and error indices decreasing by 50–67%. Furthermore, we estimated and predicted daily mappings of PM2.5 traffic source impacts using the IMSI method based on optimized concentration fields, which improved spatially resolved source contributions to PM2.5. Our findings reveal that PM2.5 traffic source impacts display significant spatial heterogeneity, and these hotspots can be precisely identified during the pollution processes with sharp changes. The evaluation results indicated that there is a good correlation (R of 0.79) between PM2.5 traffic source impacts by IMSI method and traffic source contributions apportioned by a receptor model at Beijing site. Our study provides deeper insights of estimating the spatiotemporal distribution of PM2.5 source-specific impacts especially in regions without PM2.5 compositions, which can provide more complete and timely guidance to implement precise air pollution management strategies.

A Combined Approach for Predicting the Distribution of Harmful Substances in the Atmosphere Based on Parameter Estimation and Machine Learning Algorithms

This paper proposes a new approach to predicting the distribution of harmful substances in the atmosphere based on the combined use of the parameter estimation technique and machine learning algorithms. The essence of the proposed approach is based on the assumption that the concentration values predicted by machine learning algorithms at observation points can be used to refine the pollutant concentration field when solving a differential equation of the convection-diffusion-reaction type. This approach reduces to minimizing an objective functional on some admissible set by choosing the atmospheric turbulence coefficient. We consider two atmospheric turbulence models and restore its unknown parameters by using the limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm. Three ensemble machine learning algorithms are analyzed for the prediction of concentration values at observation points, and comparison of the predicted values with the measurement results is presented. The proposed approach has been tested on an example of two cities in the Republic of Kazakhstan. In addition, due to the lack of data on pollution sources and their intensities, an approach for identifying this information is presented.

The NOx-O3 photochemical reactive air pollutant dispersion around an isolated building—the role of turbulence model and building aspect ratio

The NOx-O3 photochemical reactive air pollutant dispersion around an isolated building bears significant scientific and engineering implications. The present work extends the investigation to the role of turbulence models and building aspect ratio, providing valuable insights for future numerical investigations and building complex design. Validated against wind tunnel experiments, a serial Computational Fluid Dynamics (CFD) investigation was conducted with physiochemical coupling, including one large eddy simulation with near-wall resolution (LES-NWR) and three Reynolds-averaged Navier-Stokes (RANS) viscosity models (i.e., standard k-ε model (STK), renormalization group k-ε model (RNG), realizable k-ε model (RLZ)). The numerical models were assessed regarding the predictive capability of the mean pollutant concentration and velocity fields. Results show that the areas of high NO concentration are generally indifferent to the selection of turbulence models. Elsewhere, the RLZ provides the most comparable results to the LES-NWR, whereas the RNG and STK underestimate the reverse flow in the building wake and produce subpar predictions at the roof. The RNG also yields the weakest prediction by overestimating the lateral separation bubbles’ influence. Furthermore, changing the aspect ratio has negligible effects on the stream and spanwise dispersion of the highly concentrated pollutant. However, the building width plays a more decisive role in constraining the streamwise dispersion than the building height. Doubling the building length also slows down the shrinkage of recirculation, leading to a steep deduction in the streamwise dispersion along the building height.

Refined design of ventilation systems to mitigate infection risk in hospital wards: Perspective from ventilation openings setting

To prevent respiratory infections between patients and medical workers, the transmission risk of airborne pollutants in hospital wards must be mitigated. The ventilation modes, which are regarded as an important strategy to minimize the infection risk, are challenging to be systematically designed. Studies have considered the effect of ventilation openings (inlets/outlets) or infected source locations on the airflow distribution, pollutant removal, and infection risk mitigation. However, the relationship (such as relative distance) between ventilation openings and infected sources is critical because it affects the direct exhaust of exhaled pollutants, which has not been thoroughly studied. To explore pollutant removal and infection prevention in wards, different ventilation modes (with varying ventilation openings) and infected patient locations must be jointly considered. This study investigated displacement ventilation (DV), downward ventilation (DWV), and stratum ventilation (SV) with 4, 6, and 10 scenarios of ventilation openings, respectively. The optimal ventilation mode and relative distance between outlets and infected patients were analyzed based on the simulated pollutant concentration fields and the evaluated infection risk. The pollutant removal effect and infection risk mitigation of SV in the ward were largely improved by 75% and 59% compared with DV and DWV, respectively. The average infection risk was reduced below 7% when a non-dimensional relative distance (a ratio of the actual distance to the cubic root of the ward volume) was less than 0.25 between outlets and infected patient. This study can serve as a guide for the systematic ventilation system design in hospitals during the epidemic.

A comprehensive analysis on the source-exposure and diffusion rule of VOCs for end-life vehicles dismantling activities

In recent years, the rapid development of technology has posed significant challenges to the waste management practices of the retired vehicle industry. How to minimize the environmental impact during the recycle process of scrap vehicle has emerged as a prevalent and pressing concern. This study employed statistical analysis and positive matrix factorization (PMF) model to assess the origin of Volatile Organic Compounds (VOCs) at a scrap vehicle dismantling location situated in China. The quantification of potential hazards to human health arising from identified sources was achieved through the integration of source characteristics with exposure risk assessment. In addition, fluent simulation was employed to examine the spatiotemporal dispersion of the pollutant concentration field and velocity profile. The findings of the study revealed that the activities of parts cutting, disassembling air conditioning and refined dismantling were responsible for 89.98 %, 84.36 %, and 78.63 % of the total air pollution accumulation, respectively. Additionally, it should be noted that the aforementioned sources accounted for 59.40 %, 18.44 %, and 4.86 % of the aggregate non-cancer risk. The cumulative cancer risk was determined to be the disassembling air conditioning process, with a contribution of 82.71 %. Meanwhile, the average concentration of VOC in soil around the disassembling air conditioning area is 8.4 times higher than the background value. The simulation revealed that pollutants were primarily dispersed within the factory at a height ranging from 0.75 m to 2 m, which corresponds to the human respiratory zone and the concentration of pollutants in the vehicle cutting area was observed to exceed normal levels by over 10 times. These findings of this study may serve as a foundation for improving of environmental protection measures of industrialization.

Control of flow, thermal and pollutant concentration fields by entrainer air streams to improve fresh air quality intake into a semiconductor manufacture/ processing plant

Simulations of turbulent flow, thermal and concentration fields around a semiconductor manufacture/processing plant are reported. Monitored pollutants are ammonia (NH 3 ), hydrofluoric acid (HF), total volatile organic compounds (TVOC) and PM 2.5 that are released from chimneys on the roofs of various buildings. Some of the fresh air for the plant is supplied by the make-up air units (MAU) located on the roofs of the buildings. Returned pollutants through the MAUs can end up in the cleanroom thus decreasing the quality of wafer products. A 3-D transient large eddy simulation model was developed to predict the environment near the plant and transport of pollutants during the summer and winter seasons. Concentrations of pollutants at the inlet planes of the MAUs exhibited marked variations during the day. Pollutants with concentrations higher than industry thresholds were generally re-directed back into the plant through the six MAUs except two that were free of HF and TVOC. Modified flow, thermal and pollutant concentration fields in the vicinity of the plant brought about by introducing 12 vertically-oriented entrainer air streams (EAS) into the environment were also studied in order to quantify their effects on reducing the concentrations of pollutants at the inlet planes of the MAUs below accepted industry thresholds. It is shown that dispersion of the pollutants was markedly affected by the introduction of these novel entrainer air streams. Ingestion of pollutants into the plant was completely eliminated during (i) summer and (ii) winter except for MAU5 (NH 3 ) and MAU6 (NH 3 and TVOC) that exhibited noticeable reduction of concentrations by at least 73.3%. The proposed system can lead to cleaner production practices through eliminating product rejection, lower utilization of raw materials, reduced introduction of pollutants in the environment, etc.

Using CFD to Simulate the Concentration of Polluting and Harmful Gases in the Roadway of Non-Metallic Mines Reveals Its Migration Law

The green and pollution-free mining of resources has always been a research field that people have focused on. In the process of mining resources, the production of CO, SO2 and other pollutants directly affects the health of miners and the atmospheric environment in the mining area. Therefore, it is particularly important to deal with and control the polluting gases generated by mining. Taking the underground roadway of a coal mine in Hengdong City, Hunan Province, as the research object, we studied the migration law of pollutant gas there. Comsol software was used to determine the changing state of pollutant gas migration in the roadway, and a simulation model of the wind field and the pollutant concentration field in the roadway under turbulent conditions was established. The results showed that, when the air flow moved to the front face of the roadway, it generated backflow to form a counterclockwise-rotating air flow vortex. Here, the air flow stagnated, hindering the diffusion of pollutants. The gas moved with the air flow in the roadway, and the flow’s velocity decreased in the middle of the roadway, causing pollutants to accumulate. The whole wind field tended to be stable at a plane 25 m from the roadway’s outlet. This indicates that the middle part of the roadway is the place where the polluted gas accumulates, and it is of representative significance to study the concentration of the polluted gas in the roadway in this section.

Performance Evaluation of the RANS Models in Predicting the Pollutant Concentration Field within a Compact Urban Setting: Effects of the Source Location and Turbulent Schmidt Number

Computational Fluid Dynamics (CFD) is used to accurately model and predict the dispersion of a passive scalar in the atmospheric wind flow field within an urban setting. The Mock Urban Setting Tests (MUST) experiment was recreated in this work to test and evaluate various modeling settings and to form a framework for reliable representation of dispersion flow in compact urban geometries. Four case studies with distinct source locations and configurations are modeled using Reynolds-Averaged Navier–Stokes (RANS) equations with ANSYS CFX. The performance of three widely suggested closure models of standard k−ε, RNG k−ε, and SST k−ω is assessed by calculating and interpreting the statistical performance metrics with a specific emphasis on the effects of the source locations. This work demonstrates that the overprediction of the turbulent kinetic energy by the standard k−ε counteracts the general underpredictions by RANS in geometries with building complexes. As a result, the superiority of the standard k−ε in predicting the scalar concentration field over the two other closures in all four cases is observed, with SST k−ω showing the most discrepancies with the field measurements. Additionally, a sensitivity study is also conducted to find the optimum turbulent Schmidt number (Sct) using two approaches of the constant and locally variable values.

Influence of the Grid Resolutions on the Computer-Simulated Surface Air Pollution Concentrations in Bulgaria

The present study aims to demonstrate the effects of horizontal grid resolution on the simulated pollution concentration fields over Bulgaria. The computer simulations are performed with a set of models used worldwide—the Weather Research and Forecasting Model (WRF)—the meteorological preprocessor, the Community Multiscale Air Quality Modeling System (CMAQ)—chemical transport model, Sparse Matrix Operator Kernel Emissions (SMOKE)—emission model. The large-scale (background) meteorological data used in the study were taken from the ‘NCEP Global Analysis Data’ with a horizontal resolution of 1° × 1°. Using the ‘nesting’ capabilities of the WRF and CMAQ models, a resolution of 9 km was achieved for the territory of Bulgaria by sequentially solving the task in several consecutive nested areas. Three cases are considered in this paper: Case 1: The computer simulations result from the domain with a horizontal resolution (both of the emission source description and the grid) of 27 km.; Case 2: The computer simulations result from the domain with a horizontal resolution (both of the emission source description and the grid) of 9 km.; Case 3: A hybrid case with the computer simulations performed with a grid resolution of 9 km, but with emissions such as in the 27 km × 27 km domain. The simulations were performed, for all the three cases, for the period 2007–2014 year, thus creating an ensemble large and comprehensive enough to reflect the most typical atmospheric conditions with their typical recurrence. The numerical experiments showed the significant impact of the grid resolution not only in the pollution concentration pattern but also in the demonstrated generalized characteristics. Averaged over a large territory (Bulgaria); however, the performances for cases one and two are quite similar. Bulgaria is a country with a complex topography and with several considerably large point sources. Thus, some of the conclusions made, though based on Bulgarian-specific experiments, may be of general interest.

Deep-learning spatial principles from deterministic chemical transport models for chemical reanalysis: an application in China for PM<sub>2.5</sub>

Abstract. Well-estimated air pollutant concentration fields are critically important to compensate for observations that are only sparsely available, especially over non-urban areas. Previous data fusion methods generally used statistical models to relate observations of target variables to proxy data and supporting variables at known stations. In this study, we developed a new data fusion paradigm by designing a deep-learning model framework and workflow to learn multivariable spatial correlations from chemical transport model (CTM) simulations, before using it to estimate PM2.5 reanalysis fields from station observations. The model was composed of two modules as an explainable PointConv operation to pre-process isolated observations and a regression grid-to-grid network to build correlations among multiple variables. The model was trained with only CTM simulations and supporting geographical covariates. The trained model was evaluated in two aspects of (1) reproducing raw PM2.5 CTM simulations and (2) generating reanalysis and fused PM2.5 fields. First, the model was able to reproduce the CTM simulations well on a full domain from sampled CTM data items at sparse locations with an average R2=0.94 and RMSE = 4.85 µg m−3. Second, the fused PM2.5 fields estimated from observations achieved a good performance with R2=0.77 (RMSE = 14.29 µg m−3) and R2=0.84 (RMSE = 12.96 µg m−3) respectively evaluated at the stringent city level and station level. The generated reanalysis PM2.5 fields have complete spatial coverage within the modeling domain. One significant benefit of the fusion framework is that the model training does not rely on observations, which can be used to predict PM2.5 fields in newly set up observation networks such as those using portable sensors. Meanwhile, in the prediction procedure, only station observations are used along with supporting covariates. The fusion model has high computing efficiency (< 1 s d−1) due to acceleration using a graphical processing unit (GPU). As an alternative to generate chemical reanalysis fields, the method can be readily implemented in near-real time and be universally applied for other simulated variables with measurements available.

Estimation of pollutant dispersion around a building within non-isothermal boundary-layer using detached eddy simulation

The paper presents the numerical results of modelling pollutant transport from a low source behind a bluff-body imitating a building within a non-isothermal boundary-layer. The main goal of the study is to estimate the tracer gas dispersion in a complex turbulent separated flow behind a building in the presence of interference of the atmospheric boundary-layer and local flows. In the first part of the study we compare numerical approaches URANS and IDDES for turbulent flow prediction on a configuration for which experimental data are available. It is shown that detached eddy simulation approach predicts correctly the main separated flow features and demonstrates a reliable correlation with the experimental data on mean velocity, pollutant concentration and temperature fields. In the second part of the study, the influence of unstable thermal stratified flow on the tracer gas transport around a building is analyzed using IDDES method. The unstable thermal flow regime considered in the study affects the distribution of the pollutant concentration in the re-circulation zone behind the building. The presence of additional buoyancy effects leads to an increase in the gas concentration on the leeward wall of the body and gas transport from a ground region to a height greater than in the case with the neutral boundary-layer.

Developing air pollution concentration fields for health studies using multiple methods: Cross-comparison and evaluation

Past air pollution epidemiological studies have used a wide range of methods to develop concentration fields for health analyses. The fields developed differ considerably among these methods. The reasons for these differences and comparisons of their strengths, as well as the limitations for estimating exposures, remains under-investigated. Here, we applied nine methods to develop fields of eight pollutants (carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), fine particulate matter (PM2.5), and three speciated PM2.5 constituents including elemental carbon (EC), organic carbon (OC), and sulfate (SO4)) for the metropolitan Atlanta region for five years. The nine methods are Central Monitor (CM), Site Average (SA), Inverse Distance Weighting (IDW), Kriging (KRIG), Land Use Regression (LUR), satellite Aerosol Optical Depth (AOD), CMAQ model, CMAQ with kriging adjustment (CMAQ-KRIG), and CMAQ based data fusion (CMAQ-DF). Additionally, we applied an increasingly popular method, Random Forest (RF), and compared its results for NO2 and PM2.5 with other methods. For statistical evaluation, we focused our discussion on the temporal coefficient of determination, although other metrics are also calculated. Raw output from the CMAQ model contains modeling biases and errors, which are partially mitigated by fusing observational data in the CMAQ-KRIG and CMAQ-DF methods. Based on analyses that simulated model responses to more limited input data, the RF model is more robust and outperforms LUR for PM2.5. These results suggest RF may have potential in air pollution health studies, especially when limited measurement data are available. The RF method has several important weaknesses, including a relatively poor performance for NO2, diagnostic challenges, and computational intensiveness. The results of this study will help to improve our understanding of the strengths and weaknesses of different methods for estimating air pollutant exposures in epidemiological studies.

Incorporating gridded concentration data in air pollution back trajectories analysis for source identification

The identification of air pollution sources is a challenging task due to the confounding effects of local and transboundary emitters. While the Potential Source Contribution Function (PSCF) is a useful tool for trajectory analysis and source identification, it can overestimate the impact of less polluted areas in which a trajectory event passed through. In this work, we propose incorporating air pollutant concentration fields from reanalysis data to improve the PSCF method's performance that avoids the overcontribution of less polluted areas. We develop the 3D-PSCF-CONC based on an updated three-dimensional version of the PSCF method (3D-PSCF). We used as a test case the Metropolitan Area of São Paulo (MASP) in Brazil, one of the most polluted regions in the world, for comparing the results of the conventional and the new PSCF method. A monitoring station in Vitória - Espírito Santo, Brazil which has smaller local emissions than MASP was used as an auxiliary test case. Hourly carbon monoxide (CO) and sulfur dioxide (SO2) concentrations from 2015 to 2019 of monitoring stations were analyzed. The daily average concentrations of CO and SO2 were used as a threshold on the probability calculations. An overall of 1825 backward trajectories from HYSPLIT version 4 was processed using the three models. CO and SO2 surface concentrations from MERRA-2, with a resolution of 0.5° x 0.625° was incorporated in the 3D-PSCF-CONC to correct the potential contribution calculation based on the air pollutant concentration field. Our results revealed that the 3D-PSCF-CONC reached a more consistent identification of local and regional sources, rather than emphasizing mostly the regional sources as the other PSCF methods. The 3D-PSCF-CONC reduced the potential contribution of less polluted and high precipitation areas. The analysis suggests a contribution of vehicular sources close to the receptor site in MASP and long-range transport of industrial emissions and biomass burning from the land clearing of sugarcane production. This work presents an important tool for understanding the air pollution process and source identification.

From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors

Low-cost sensors for particulate matter mass (PM) enable spatially dense, high temporal resolution measurements of air quality that traditional reference monitoring cannot. Low-cost PM sensors are especially beneficial in low and middle-income countries where few, if any, reference grade measurements exist and in areas where the concentration fields of air pollutants have significant spatial gradients. Unfortunately, low-cost PM sensors also come with a number of challenges that must be addressed if their data products are to be used for anything more than a qualitative characterization of air quality. The various PM sensors used in low-cost monitors are all subject to biases and calibration dependencies, corrections for which range from relatively straightforward (e.g. meteorology, age of sensor) to complex (e.g. aerosol source, composition, refractive index). The methods for correcting and calibrating these biases and dependencies that have been used in the literature likewise range from simple linear and quadratic models to complex machine learning algorithms. Here we review the needs and challenges when trying to get high-quality data from low-cost sensors. We also present a set of best practices to follow to obtain high-quality data from these low-cost sensors.

Numerical simulation of the effects of canopy properties on airflow and pollutant dispersion in street canyons

The air flow and pollutant concentration fields in a street canyon affected by trees could affect the comfort and health of residents. At present, the description of the non-uniform/discontinuous distribution of leaves is difficult. In this study, the leaf distribution in the canopy was characterized by establishing non-continuous (uniform/random) algorithm based on a numerical simulation method, and the effects of canopy properties including, height, porosity and uniform/random leaf distribution, on the airflow and pollutant concentration fields in urban street canyons were investigated. The position of the tree canopy was found to directly affect the airflow field form and the air velocity distribution in the street canyon at low inflows. The average air velocity in the street canyon could be reduced significantly when the top of the tree canopy is near the top of the street canyon. The air velocity and pollutant concentration in the street canyon would vary only slightly due to the canopy porosity. Due to the increasing canopy porosity, the air velocity would increase, and the pollutant concentration would be reduced. The leaves are non-continuous and uniformly distributed at constant porosity, which does not significantly change the velocity distribution and pollutant concentration in the street canyon.

Mathematical modeling of the spread of pollutants in the atmosphere from the storage of liquid waste of pig complexes during reagent treatment

The effect of treatment of liquid organic waste from pig farms with various calcium-containing reagents, followed by neutralization to pH 7.0-8.5, on reducing the emissions of pollutants such as ammonia, hydrogen sulfide, methyl mercaptan, nitrogen and carbon oxides at various stages of treatment was studied. The article deals with mathematical modeling of changes in the concentration fields of pollutants over time. Dynamic processes of gaseous pollutants propagation in the surface air layer above liquid organic waste accumulators are simulated. The developed model will allow calculating the distribution of pollutants over the storage of liquid organic waste in a given period of time and calculate the concentrations of gaseous pollutants in steady-state and non-stationary modes during reagent treatment. The time of reducing the concentrations of pollutants to the required values is determined based on the results of a computational experiment in the VITECON software package. The corrected sizes of sanitary protection zones of the pig complex before and after reagent treatment are presented.

Modeling of the dispersion of pollutants in porous media: Case of a landfill in Brazil

Municipal solid waste (MSW) disposal systems are still a sanitary and environmental problem. The high concentration of toxic substances existing in liquids arising from the decomposition of waste, present a risk to human health and the environment. It is necessary to establish mechanisms to predict leachate dispersion in order to determine possible scenarios and control systems. This work investigates the dispersion of leachate from urban solid waste through saturated soil. The municipal landfill of Jacarezinho in Brazil is the focus of the study. The conceptual model was based on existing data from geology, hydrology, hydrogeology and leachate characterization. The codes MODFLOW and MT3DMS were used and the dispersion of chromium, copper, lead and zinc was simulated. Pollutant mass concentration fields were calculated in 1, 5, 10, 25 and 50 years. The results indicated that the pollutants could reach the Ouro Grande River, in a period of 5 years and the tributary in a period of 1 year of the initial generation of leachate. For the final period of the simulation, the tributary of the Ouro Grande River received copper and lead concentrations that exceeded the maximum values ​​allowed in Brazilian legislation for the watercourse quality standard. It was concluded that the Jacarezinho landfill has the potential to pollute the surrounding soil and groundwater.

Simulation of indoor pollutant diffusion concentration field in passive house based on CFD software

Due to the good air tightness of the passive house, once there are poor quality furniture materials in the room, it is easy to cause indoor formaldehyde pollution which do harm to people’s health as well as the quality of their life. This article introduces the CFD software to simulate the change of indoor formaldehyde concentration in the passive house when opening the fresh air system to ensure indoor air quality and to employ a new way to the better quality of life of residents. The function of CFD software and two fluid dynamics control equations are introduced in the essay, and the finite difference method of control equation discretization is studied as well. Based on CFD software, the case is analyzed, under which the indoor formaldehyde concentration changes, according to different fresh air system inlet locations. The simulation results show that when the two air outlets of the living room are on the side of the sofa and TV cabinet, and the air outlet of the bedroom is in the corner, the formaldehyde purification rate is better, which a more reasonable air supply method is.

Study on Pollutant Emission in a Natural Draft Dry Cooling tower with Flue Gas Injection based on LES

This study investigated the Computational Fluid Dynamics (CFD) simulation of pollutant emission in a natural draft dry cooling tower (NDDCT) with flue gas injection. In order to predict the diffusion and distribution characteristics of the pollutant more accurately, Large Eddy Simulation (LES) was applied to predict the flow field and pollutant concentration field and compared with Reynolds Average Navier-Stokes (RANS). The relationship between pollutant concentration pulsation and velocity pulsation is emphatically analyzed. The results show that the maximum value of LES is about 43 times that of RANS for the prediction of pollutant concentration in the inner shell of cooling tower. Compared with RANS, LES can simulate flow field pulsation with a smaller scale and higher frequency.