Mean Fractional Bias Research Articles

Hybrid machine learning modeling of nitrogen removal from wastewater using gas-liquid-solid circulating fluidized bed riser

Municipal wastewater generally contains high amounts of nitrogen (N), approximately 23–28 mg/L of total Kjeldahl Nitrogen (TKN), which causes eutrophication and pollution of groundwater. This article reports the generation of empirical models for estimating the nitrogen removal efficacy of two outputs (NH4-N and NO3-N) from the anoxic riser of a highly efficient municipal wastewater treatment system involving a gas-liquid-solid circulating fluidized bed (GLSCFB) bioreactor. To identify the important variables and discard irrelevant ones, the ReliefF algorithm is applied initially. Out of 10 independent variables, this method indicates that only three, such as flow rate, total Kjeldahl Nitrogen (TKN), and downer effluent of NO3-N, are meaningful for anoxic nitrogen removal. Then, a hybrid Bayesian Optimization Algorithm and Support Vector Regression (BOA-SVR) is implemented, considering only the essential variables. In this regard, real-life laboratory data are utilized to develop the models. The BOA approach and k-fold cross-validation technique are explicitly applied to optimize the model's hyperparameters and avoid overfitting. The established models provided sufficiently accurate predictions via their comparisons to the experimental observations. Besides, the BOA-SVR model outperforms the classical multiple linear regression (MLR). The BOA-SVR models' forecasted results for both the predicted amount of NH4-N and NO3-N are favorably compared with the laboratory trials (R2> 99 %). A comprehensive evaluation is further conducted to validate the performance of this model by calculating the root mean square error, mean absolute percentage error, fractional bias, and computational efficiency. Gaussian white noise is used to add three distinct noise levels (20 %, 40 %, and 60 %) to the testing data to evaluate the model's robustness. Moreover, the plots of relative deviations and residuals were dispersed across the zero-reference line with moderate fluctuations. Sensitivity analysis indicated the relative importance of the three independent variables on the anoxic nitrogen removal in the order of influent TKN > downer effluent NO3-N >flowrate. Overall, the developed tool has substantial potential in modeling other convoluted processes.

A New CCN Number Concentration Prediction Method Based on Multiple Linear Regression and Non‐Negative Matrix Factorization: 1. Development, Validation, and Comparison Using the Measurement Data Over the Korean Peninsula

AbstractTo reduce uncertainty in climate change prediction, a large amount of cloud condensation nuclei number concentration (NCCN) data must be obtained. This study aimed to develop a new NCCN prediction method, hereafter MLRNMF method, that applies multiple linear regression (MLR) and non‐negative matrix factorization (NMF) to aerosol number size distribution data measured in Seoul and over the Yellow Sea. To verify the reliability of the MLRNMF method, a data set separated from the training data set was used, and sufficient time differences of several years were given between the two data sets to make them as independent as possible from each other. The predicted NCCN was in acceptable agreement with the measured NCCN. The coefficient of determination (R2) values between measured and predicted NCCN for the Yellow Sea and Seoul were 0.81 and 0.71, respectively. Mean fractional bias (MFB) and mean fractional error (MFE) also met the performance goals (<±30% and <+50%, respectively). The MLRNMF method had similar accuracy to the backward integration method but showed strength in robustness and the capability to take into account external mixing. The NCCN prediction methods trained using data of a specific season tended to underestimate/overestimate somewhat, but MFB and MFE for all four seasons met the performance goals except for MFB in using June–August (JJA) data, implying that the MLRNMF method trained using only the data of a specific season can predict NCCN for all four seasons to some extent. It is expected that abundant NCCN data can be obtained through the MLRNMF method in future studies.

Atmospheric 137Cs dispersion following the Fukushima Daiichi nuclear accident: Local-scale simulations using CALMET and LAPMOD

This paper investigates a local-scale radionuclide dispersion modeling, focusing on the 20 km range of the Fukushima accident. The California Meteorological Model (CALMET) was used to calculate the diagnostic wind fields for simulation using on-site meteorological observations, and the Lagrangian Particle Model (LAPMOD) was used to simulate the dispersion of the 137Cs. The simulation was validated against the measurement from two 137Cs airborne concentration stations (Futaba and Naraha). A comprehensive sensitivity analysis was implemented to the source term, the number of particles per second, and the disturbance in wind speed and direction. The results show that the simulation agrees well with the measurement at Futaba (Geometric Mean Bias: 0.79, Fractional Mean Bias: 0.58) than that at Naraha (Geometric Mean Bias: 1.01, Fractional Mean Bias: 0.52), especially for the peak values. The sensitivity analysis indicates that the simulation is highly affected by the source term and the wind direction.

Retrieval of Normalized Gamma Size Distribution Parameters Using Precipitation Imaging Package (PIP) Snowfall Observations during ICE-POP 2018

Abstract Parameters of the normalized gamma particle size distribution (PSD) have been retrieved from the Precipitation Image Package (PIP) snowfall observations collected during the International Collaborative Experiment–PyeongChang Olympic and Paralympic winter games (ICE-POP 2018). Two of the gamma PSD parameters, the mass-weighted particle diameter Dmass and the normalized intercept parameter NW, have median values of 1.15–1.31 mm and 2.84–3.04 log(mm−1 m−3), respectively. This range arises from the choice of the relationship between the maximum versus equivalent diameter, Dmx–Deq, and the relationship between the Reynolds and Best numbers, Re–X. Normalization of snow water equivalent rate (SWER) and ice water content W by NW reduces the range in NW, resulting in well-fitted power-law relationships between SWER/NW and Dmass and between W/NW and Dmass. The bulk descriptors of snowfall are calculated from PIP observations and from the gamma PSD with values of the shape parameter μ ranging from −2 to 10. NASA’s Global Precipitation Measurement (GPM) mission, which adopted the normalized gamma PSD, assumes μ = 2 and 3 in its two separate algorithms. The mean fractional bias (MFB) of the snowfall parameters changes with μ, where the functional dependence on μ depends on the specific snowfall parameter of interest. The MFB of the total concentration was underestimated by 0.23–0.34 when μ = 2 and by 0.29–0.40 when μ = 3, whereas the MFB of SWER had a much narrower range (from −0.03 to 0.04) for the same μ values.

The importance of ammonia for springtime atmospheric new particle formation and aerosol number abundance over the United States

New particle formation (NPF) and subsequent growth can contribute upwards of 50 % of the global cloud condensation nuclei (CCN) budget. It is also a significant source of ultrafine aerosols (PM0.1) with health implications. Ammonia (NH3) can play a significant role in enhancing NPF and contributing to the growth of nucleated particles. Understanding these processes are vital for air quality and climate. Here, we examine the role of NH3 in NPF and consequent effects on aerosol number concentrations (including CCN) and size distributions during springtime over the United States (US). We use the GEOS-Chem chemistry transport model coupled with the size-resolved Advanced Particle Microphysics (APM) Model. We also employ measurements of particle number size distributions, CN10 (condensation nuclei > 10 nm), CCN0.4 (CCN at 0.4 % supersaturation), and aerosol composition (SO4, NO3, NH4, Organics) at the Southern Great Plains site (SGP). The impact of NH3 in ion-mediated nucleation is the improved capturing of the occurrence of almost all springtime (March–April) NPF events observed at SGP during 2015–2020. Furthermore, this brings the magnitude and temporal variations of particle number concentrations in stronger agreement with observations; mean fractional bias for modeled CN10(CCN0.4) reducing from −1.26 to −0.27 (−0.75 to −0.54) and overall good-agreement (∣FractionalBias ∣ < 0.6) improving from 8.5 to 54 % (31 to 42 %). The contribution of NH3 in new particle formation is important for springtime abundance of ultrafine aerosols (explaining 63 ± 15 % of CN10) and CCN (16 ± 10 % of CCN0.4) over the US. Our analysis shows that the deviation of CCN0.4 is strongly correlated with PM1-NH4+ deviations, suggesting the importance of improved model representation of ammonium for more accurate quantification of potential cloud forming particles.

Application and testing of a windblown dust module for a bare riverbed

To accurately predict the occurrence of riverbed dust events, a high-grid resolution (1 km2) riverbed dust module was constructed for this study. Although the module used the built-in CMAQ (Community Multiscale Air Quality model) dust module, the critical parameter values in the module were adjusted according to the experimental data for Taiwan. The results showed that the riverbed dust module can accurately predict the occurrence time of riverbed dust and thus improve the prediction of CMAQ's for the PM10 concentrations of downwind riverbed dust. The mean fractional bias and mean fractional error improved from −77 and 84% to −5 and 57%, respectively. Seven sensitivity-case simulations were performed to understand which parameters and formulas were important. The results are summarized as follows. (1) Selecting an appropriate horizontal sand flux estimation formula/vertical-to-horizontal dust flux ratio estimation formula/surface roughness length value is important because it can significantly affect dust emissions estimation. A local experimental value for the surface roughness length value is preferred. (2) For bare river land, it was acceptable to use the default value (= 1) for the surface roughness correction factor value. (3) In environments with high soil moisture, special attention should be paid to setting the threshold soil moisture value. (4) Different river locations may require different parameter values to be set within the module, and the ideal threshold friction velocity is an important parameter. (5) The estimation and use of gusts can improve the prediction of intense dust emissions; however, the prediction of hourly gusts must be improved.

Climatology and model prediction of aerosol optical properties over the Indo-Gangetic Basin in north India.

The current research focuses on the use of different simulation techniques in the future prediction of the crucial aerosol optical properties over the highly polluted Indo-Gangetic Basin in the northern part of India. The time series model was used to make an accurate forecast of aerosol optical depth (AOD) and angstrom exponent (AE), and the statistical variability of both cases was compared in order to evaluate the effectiveness of the model (training and validation). For this, different models were used to simulate the monthly average AOD and AE over Jaipur, Kanpur and Ballia during the period from 2003 to 2018. Further, the study was aimed to construct a comparative model that will be used for time series statistical analysis of MODIS-derived AOD550 and AE412-470. This will provide a more comprehensive information aboutthe levels of AOD and AE that will exist in the future. To test the validity and applicability of the developed models, root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), fractional bias (FB), and Pearson coefficient (r) were used to show adequate accuracy in model performance. From the observation, themonthly mean values of AOD and AE were found to be nearly similar at Kanpur and Ballia (0.62 and 1.26) and different at Jaipur (0.25 and 1.14). Jaipur indicates that during the pre-monsoon season, the AOD mean value was found to be highest (0.32 ± 0.15), while Kanpur and Ballia display higher AOD mean values during the winter season (0.72 ± 0.26 and 0.83 ± 0.32, respectively). Among the different methods, the autoregressive integrated moving average (ARIMA) model was found to be the best-suited model for AOD prediction at Ballia based on fitted error (RMSE (0.22), MAE (0.15), MAPE (24.55), FB (0.05)) and Pearson coefficient r (0.83). However, for AE, best prediction was found at Kanpur based on RMSE (0.24), MAE (0.21), MAPE (22.54), FB (-0.09) and Pearson coefficient r (0.82).

Regional Predictions of Air Pollution in Guangzhou: Preliminary Results and Multi-Model Cross-Validations

A precise air pollution forecast is the basis for targeted pollution control and sustained improvements in air quality. It is desirable and crucial to select the most suitable model for air pollution forecasting (APF). To achieve this goal, this paper provides a comprehensive evaluation of performances of different models in simulating the most common air pollutants (e.g., PM2.5, NO2, SO2, and CO) in Guangzhou (23.13° N, 113.26° E), China. To simulate temporal variations of the above-mentioned air pollutant concentrations in Guangzhou in September and October 2020, we use a numerical forecasting model (i.e., the Weather Research and Forecasting model with Chemistry (WRF-Chem)) and two artificial intelligence models (i.e., the back propagation neural network (BPNN) model and the long short-term memory (LSTM) model). WRF-Chem is also used to simulate the meteorological elements (e.g., the 2 m temperature (T2), 2 m relative humidity (RH), and 10 m wind speed and direction (WS, WD)). In order to investigate the simulation accuracies of classical APF models, we simultaneously compare the simulations of the WRF-Chem, BPNN, and LSTM models to ground truth observations. Comparative assessment results show that WRF-Chem simulated air pollutant (i.e., PM2.5, NO2, SO2, and CO) concentrations have the best correlations with ground measurements (i.e., Pearson correlation coefficient R = 0.88, 0.73, 0.61, and 0.61, respectively). Furthermore, to evaluate model performance in terms of accuracy and stability, the normalized mean bias (NMB, %) and mean fractional bias (MFB, %) are adopted as the standard performance metrics (SPMs) proposed by Boylan et al. The comparison results indicate that when simulating PM2.5, WRF-Chem was more effective than the BPNN but less effective than the LSTM. While simulating concentrations of NO2, SO2, and CO, the WRF-Chem model performed better than the BPNN and LSTM models. With regards to WRF-Chem, the NMBs and MFBs for the PM2.5 simulations are, respectively, 6.49% and 0.02%, –11.96% and –0.031% for NO2, 7.93% and 0.019% for CO, and 5.04% and 0.012% for SO2. Our results suggest that WRF-Chem has superior performance and better accuracy than the NN-based prediction models, making it a promising and useful tool to accurately predict and forecast regional air pollutant concentrations on a city scale.

Development of a Dust Source Map for WRF-Chem Model Based on MODIS NDVI

We present the development of a physically-based dust source map for the GOCART-AFWA dust module in WRF-Chem model. The new parameterization is based on MODIS-NDVI and an updated emission strength map is computed every 15 days from the latest satellite observations. Modeling simulations for the period April–May 2017 over the Mediterranean, north Africa, and the Middle East are compared with observations of AOD at 31 AERONET stations. The new module is capable of reproducing the dust sources at finer detail. The overall performance of the model is improved, especially for stronger dust episodes with AOD &gt; 0.25. For this threshold the model BIAS decreases from −0.20 to −0.02, the RMSE from 0.38 to 0.30, the Correlation Coefficient improves from 0.21 to 0.47, the fractional gross error (FGE) from 0.62 to 0.40, and the mean fractional bias (MFB) from −0.49 to −0.08. Similar improvement is also found for the lower AOD thresholds (&gt;0.0 and &gt;0.1), especially for the stations in Europe, the Mediterranean, Sahel, the Middle East, and Arabian Peninsula, which are mostly affected by dust transport during the experimental period. An overprediction of AOD, compared to the original dust-source scheme, is found for some stations in the Sahara desert, the Atlantic Ocean, and the Iberian Peninsula. In total, 124 out of the 170 statistical scores that are calculated indicate improvement of model performance.

Estimating NOx LOTOS-EUROS CTM Emission Parameters over the Northwest of South America through 4DEnVar TROPOMI NO2 Assimilation

In this work, we present the development of a 4D-Ensemble-Variational (4DEnVar) data assimilation technique to estimate NOx top-down emissions using the regional chemical transport model LOTOS-EUROS with the NO2 observations from the TROPOspheric Monitoring Instrument (TROPOMI). The assimilation was performed for a domain in the northwest of South America centered over Colombia, and includes regions in Panama, Venezuela and Ecuador. In the 4DEnVar approach, the implementation of the linearized and adjoint model are avoided by generating an ensemble of model simulations and by using this ensemble to approximate the nonlinear model and observation operator. Emission correction parameters’ locations were defined for positions where the model simulations showed significant discrepancies with the satellite observations. Using the 4DEnVar data assimilation method, optimal emission parameters for the LOTOS-EUROS model were estimated, allowing for corrections in areas where ground observations are unavailable and the region’s emission inventories do not correctly reflect the current emissions activities. The analyzed 4DEnVar concentrations were compared with the ground measurements of one local air quality monitoring network and the data retrieved by the satellite instrument Ozone Monitoring Instrument (OMI). The assimilation had a low impact on NO2 surface concentrations reducing the Mean Fractional Bias from 0.45 to 0.32, primordially enhancing the spatial and temporal variations in the simulated NO2 fields.

Impact of different urban canopy models on air quality simulation in Chengdu, southwestern China

Urban air pollution has emerged as a prominent public health concern in megacities and highly developed city clusters. Accurate modelling of urban air quality over complex terrain is challenging due to heterogeneous urban landscapes and multiscale land-atmosphere interactions. In this study, we investigated the applicability of urban canopy models in the Weather Research and Forecast (WRF) model and assessed the impacts of implementing these models on the urban air quality simulation in the Community Multiscale Air Quality (CMAQ) model over the megacity Chengdu, southwestern China. The land use and land cover of Chengdu were updated in WRF by using the land-use products in 2017 from the Moderate-resolution Imaging Spectroradiometer (MODIS). Sensitivity experiments with various urban canopy models were conducted to investigate the feasibility of different urban canopy models on WRF-CMAQ simulations. We found that the SLAB model significantly underestimates NO2 and PM2.5 concentrations, with mean fractional bias in winter (summer) reaching 52.93% (−50.34%) and −102.82% (−23.12%), respectively. Such large biases are mainly attributed to overpredicted wind speeds resulting from the flat structure in the SLAB model. In contrast, the BEM (a multilayer urban canopy model coupled with air-conditioning systems) model yields the best model performance in both winter and summer, with mean fractional errors of 33.15% (38.96%) and 34.10% (33.15%) for NO2 and PM2.5 in winter (summer), respectively. The UCM (a single-layer urban canopy model) model illustrates good performance in summer, with MFBs of 25.61% for NO2 and 19.03% for PM2.5, while NO2 and PM2.5 concentrations are overestimated in winter, with MFBs of 62.58% and 38.19%, respectively. In contrast, BEP (a multilevel urban canopy model)-modelled NO2 (MFB: 37.18%) and PM2.5 (MFB: 18.72%) correlate well with observations in winter, while significantly overestimated air pollutant concentrations in summer with MFBs of NO2 and PM2.5 of 49.70% and 44.50%, respectively. In general, the BEP model and the BEM model are well suited for air quality simulations over Chengdu in winter, and the BEM model could be considered for air quality simulations in summer. Furthermore, we assessed the effects of extensive usage of air conditioning systems in Chengdu during summertime, and the results suggest that using air conditioning systems facilitates the dispersion of air pollutants over Chengdu. This study pinpoints the limitations of default WRF configurations and tests the applicability of urban canopy models in the WRF-CMAQ model over Chengdu, in addition highlighting the crucial role of urban canopy models in urban meteorological-air quality simulations.

Hybrid support vector regression and crow search algorithm for modeling and multiobjective optimization of microalgae-based wastewater treatment

Microalgae-based wastewater treatment (and biomass production) is an environmentally benign and energetically efficient technique as compared to traditional practices. The present study is focused on optimization of the major treatment variables such as temperature, light-dark cycle (LD), and nitrogen (N)-to-phosphate (P) ratio (N/P) for the elimination of N and P from tertiary municipal wastewater utilizing Chlorella kessleri microalgae species. In this regard, a hybrid support vector regression (SVR) technique integrated with the crow search algorithm has been applied as a novel modeling/optimization tool. The SVR models were formulated using the experimental data, which were furnished according to the response surface methodology with Box-Behnken Design. Various statistical indicators, including mean absolute percentage error, Taylor diagram, and fractional bias, confirmed the superior performance of SVR models as compared to the response surface methodology (RSM) and generalized linear model (GLM). Finally, the best SVR model was hybridized with the crow search algorithm for single/multi-objective optimizations to acquire the global optimal treatment conditions for maximum N and P removal efficiencies. The best-operating conditions were found to be 29.3°C, 24/0 h/h of LD, and 6:1 of N/P, with N and P elimination efficiencies of 99.97 and 93.48%, respectively. The optimized values were further confirmed by new experimental data.

Vertical assessment of the mineral dust optical and microphysical properties as retrieved from the synergy between polarized micro-pulse lidar and sun/sky photometer observations using GRASP code

The potential of the synergetic use of both polarized Micro-Pulse Lidar (P-MPL) and Sun/sky photometer observations by using the GRASP (Generalized Retrieval of Atmosphere and Surface Properties) code for retrieving dust properties has been analysed in this work. Both the columnar and height-resolved variables have been evaluated in comparison, respectively, with AERONET data and vertical P-MPL-derived profiles of the dust optical and microphysical properties over a mid-latitude site. The linear regression analysis together with the mean fractional bias values and relative differences between datasets have been used as the statistical proxies for assessing the degree of agreement by examining 24 selected dust cases. Regarding columnar variables, the microphysical properties (both the particle volume concentration and effective radius) presented a high confidence level between GRASP and AERONET retrievals by taking into account all the proxies. In addition, the single scattering albedo and the real refractive index presented a good confidence level at all the wavelengths, as well, unlike the imaginary refractive index, which was confident at 440 nm only. By analysing the height-resolved properties of the total particle backscatter coefficient and the total volume concentration, the degree of agreement shows a high confidence between GRASP retrievals and P-MPL-derived variables, which were retrieved by using an alternative, well-validated, polarization-based methodology. Indeed, these results can be essential for application with no-polarized systems (e.g., ceilometers, lidars …). Overall, the synergy between P-MPL and Sun/sky photometer measurements regarding the usual co-location of both MPLNET and AERONET networks in combination with GRASP code represent an improved approach to derive the height-resolved optical and microphysical properties for mineral dust particles with a high degree of confidence.

Extreme Aerosol Events Over Eastern North America: 1. Characterizing and Simulating Historical Events

AbstractImproved characterization of the spatiotemporal extent, intensity, and causes of extreme aerosol optical depth events is critical to quantifying their regional climate forcing and the link to near‐surface air quality. An analysis of regional‐scale extreme aerosol events over the eastern United States is undertaken using output from the Modern‐Era Retrospective analysis for Research and Applications, version 2 (MERRA‐2) reanalysis and observations from the MODerate resolution Imaging Spectroradiometers (MODIS). Six extreme aerosol optical depth (AOD) events during 2003–2007, dominated by anthropogenic emissions and characterized by a regional scale extent, are identified and simulated using the Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem) applied at 12 km resolution. Statistical analyses show output from WRF‐Chem during these events is generally negatively biased in terms of the mean AOD and PM2.5, but WRF‐Chem exhibits skill in capturing the peak AOD. WRF‐Chem also exhibits fidelity in reproducing the spatiotemporal characteristics of the extreme AOD events in intensity, location of centroid, propagation, duration, and their spatial extension. Considerable event‐to‐event variability in model skill in simulating spatial patterns of extreme events is observed, with the highest spatial correlation with MERRA‐2 AOD noted for events centered in the Midwest. Mean fractional bias in modeled peak AOD is minimized for the most intense events and for events centered over the southeastern USA. WRF‐Chem output is also negatively biased in downwelling shortwave radiation at the ground and specific humidity consistent with a positive bias in simulated precipitation relative to MERRA‐2.

Late-spring and summertime tropospheric ozone and NO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; in western Siberia and the Russian Arctic: regional model evaluation and sensitivities

Abstract. We use a regional chemistry transport model (Weather Research and Forecasting model coupled with chemistry, WRF-Chem) in conjunction with surface observations of tropospheric ozone and Ozone Monitoring Instrument (OMI) satellite retrievals of tropospheric column NO2 to evaluate processes controlling the regional distribution of tropospheric ozone over western Siberia for late spring and summer in 2011. This region hosts a range of anthropogenic and natural ozone precursor sources, and it serves as a gateway for near-surface transport of Eurasian pollution to the Arctic. However, there is a severe lack of in situ observations to constrain tropospheric ozone sources and sinks in the region. We show widespread negative bias in WRF-Chem tropospheric column NO2 when compared to OMI satellite observations from May–August, which is reduced when using ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants) v5a emissions (fractional mean bias (FMB) = −0.82 to −0.73) compared with the EDGAR (Emissions Database for Global Atmospheric Research)-HTAP (Hemispheric Transport of Air Pollution) v2.2 emissions data (FMB = −0.80 to −0.70). Despite the large negative bias, the spatial correlations between model and observed NO2 columns suggest that the spatial pattern of NOx sources in the region is well represented. Scaling transport and energy emissions in the ECLIPSE v5a inventory by a factor of 2 reduces column NO2 bias (FMB = −0.66 to −0.35), but with overestimates in some urban regions and little change to a persistent underestimate in background regions. Based on the scaled ECLIPSE v5a emissions, we assess the influence of the two dominant anthropogenic emission sectors (transport and energy) and vegetation fires on surface NOx and ozone over Siberia and the Russian Arctic. Our results suggest regional ozone is more sensitive to anthropogenic emissions, particularly from the transport sector, and the contribution from fire emissions maximises in June and is largely confined to latitudes south of 60∘ N. Ozone dry deposition fluxes from the model simulations show that the dominant ozone dry deposition sink in the region is to forest vegetation, averaging 8.0 Tg of ozone per month, peaking at 10.3 Tg of ozone deposition during June. The impact of fires on ozone dry deposition within the domain is small compared to anthropogenic emissions and is negligible north of 60∘ N. Overall, our results suggest that surface ozone in the region is controlled by an interplay between seasonality in atmospheric transport patterns, vegetation dry deposition, and a dominance of transport and energy sector emissions.

Influence of biomass burning vapor wall loss correction on modeling organic aerosols in Europe by CAMx v6.50

Abstract. Increasing evidence from experimental studies suggests that the losses of semi-volatile vapors to chamber walls could be responsible for the underestimation of organic aerosol (OA) in air quality models that use parameters obtained from chamber experiments. In this study, a box model with a volatility basis set (VBS) scheme was developed, and the secondary organic aerosol (SOA) yields with vapor wall loss correction were optimized by a genetic algorithm based on advanced chamber experimental data for biomass burning. The vapor wall loss correction increases the SOA yields by a factor of 1.9–4.9 and leads to better agreement with measured OA for 14 chamber experiments under different temperatures and emission loads. To investigate the influence of vapor wall loss correction on regional OA simulations, the optimized parameterizations (SOA yields, emissions of intermediate-volatility organic compounds from biomass burning, and enthalpy of vaporization) were implemented in the regional air quality model CAMx (Comprehensive Air Quality Model with extensions). The model results from the VBS schemes with standard (VBS_BASE) and vapor-wall-loss-corrected parameters (VBS_WLS), as well as the traditional two-product approach, were compared and evaluated by OA measurements from five Aerodyne aerosol chemical speciation monitor (ACSM) or aerosol mass spectrometer (AMS) stations in the winter of 2011. An additional reference scenario, VBS_noWLS, was also developed using the same parameterization as VBS_WLS except for the SOA yields, which were optimized by assuming there is no vapor wall loss. The VBS_WLS generally shows the best performance for predicting OA among all OA schemes and reduces the mean fractional bias from −72.9 % (VBS_BASE) to −1.6 % for the winter OA. In Europe, the VBS_WLS produces the highest domain average OA in winter (2.3 µg m−3), which is 106.6 % and 26.2 % higher than VBS_BASE and VBS_noWLS, respectively. Compared to VBS_noWLS, VBS_WLS leads to an increase in SOA by up to ∼80 % (in the Balkans). VBS_WLS also leads to better agreement between the modeled SOA fraction in OA (fSOA) and the estimated values in the literature. The substantial influence of vapor wall loss correction on modeled OA in Europe highlights the importance of further improvements in parameterizations based on laboratory studies for a wider range of chamber conditions and field observations with higher spatial and temporal coverage.

Satellite-based Emission Inventory Adjustments Improve Simulations of Long-range Transport Events

Long-range pollution transport (LRT) events have a wide impact across East Asia, but are often difficult to track due to imprecise emission inventories and changing domain scales as the plume moves from source to receptor locations. This study adjusts a bottom-up emission inventory based on changes in remotely sensed NO2 column densities for a source region of East Asia, then with CMAQv5.2.1 simulates transport of LRT plumes to Taiwan. Adjustment of an emissions inventory based on satellite measurements during the COVID-19 lockdown in China led to a ~59% reduction in emissions over the relevant source area in China compared to base emissions. As a result, PM2.5 mass concentrations were reproduced to match observations (mean fractional bias, MFB of -13.9% and 18.5% at a remote and urban station) as the plume passed through northern Taiwan. Furthermore, the OMI-adjusted emissions simulation brought all of the major PM2.5 components to within ~50% of the measured values. Another LRT event from 2018 with more subtle OMI-adjustments to the emissions was also simulated and with improved overall PM2.5 mass concentration at the northern tip of Taiwan (MFB: -91.5%) compared to the base model (MFB: -102.1%), and an acceptable index of agreement (0.78). For the 2018 event, non sea-salt sulfate concentrations were consistently underpredicted (0.2–0.4), while nitrate concentrations were overpredicted by up to factor of 11. Copernicus Atmosphere Monitoring Service (CAMS) reanalysis of the PM2.5 concentrations shows high sulfate concentrations in eastern China in the areas associated with 72-h back-trajectories from northern Taiwan during both events, lending support for future model investigations of sulfate source area production and transport to Taiwan. In order to better track these LRT events out of East Asia and optimize OMI-adjustment methodology, it is recommended to explore other satellite-based products to map unaccounted for SO2 sources upstream of Taiwan.

Application of Artificial Intelligence (AI) for Sustainable Highway and Road System

There are several environmental factors such as temperature differential, moisture, oxidation, etc. that affect the extended life of the modified asphalt influencing its desired adhesive properties. Knowledge of the properties of asphalt adhesives can help to provide a more resilient and durable asphalt surface. In this study, a hybrid of Bayesian optimization algorithm and support vector regression approach is recommended to predict the adhesion force of asphalt. The effects of three important variables viz., conditions (fresh, wet and aged), binder types (base, 4% SB, 5% SB, 4% SBS and 5% SBS), and Carbon Nano Tube doses (0.5%, 1.0% and 1.5%) on adhesive force are taken into consideration. Real-life experimental data (405 specimens) are considered for model development. Using atomic force microscopy, the adhesive strength of nanoscales of test specimens is determined according to functional groups on the asphalt. It is found that the model predictions overlap with the experimental data with a high R2 of 90.5% and relative deviation are scattered around zero line. Besides, the mean, median and standard deviations of experimental and the predicted values are very close. In addition, the mean absolute Error, root mean square error and fractional bias values were found to be low, indicating the high performance of the developed model.

Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements

Abstract. Cloud condensation nuclei (CCN) number concentrations are an important aspect of aerosol–cloud interactions and the subsequent climate effects; however, their measurements are very limited. We use a machine learning tool, random decision forests, to develop a random forest regression model (RFRM) to derive CCN at 0.4 % supersaturation ([CCN0.4]) from commonly available measurements. The RFRM is trained on the long-term simulations in a global size-resolved particle microphysics model. Using atmospheric state and composition variables as predictors, through associations of their variabilities, the RFRM is able to learn the underlying dependence of [CCN0.4] on these predictors, which are as follows: eight fractions of PM2.5 (NH4, SO4, NO3, secondary organic aerosol (SOA), black carbon (BC), primary organic carbon (POC), dust, and salt), seven gaseous species (NOx, NH3, O3, SO2, OH, isoprene, and monoterpene), and four meteorological variables (temperature (T), relative humidity (RH), precipitation, and solar radiation). The RFRM is highly robust: it has a median mean fractional bias (MFB) of 4.4 % with ≈96.33 % of the derived [CCN0.4] within a good agreement range of -60%&lt;MFB&lt;+60% and strong correlation of Kendall's τ coefficient ≈0.88. The RFRM demonstrates its robustness over 4 orders of magnitude of [CCN0.4] over varying spatial (such as continental to oceanic, clean to polluted, and near-surface to upper troposphere) and temporal (from the hourly to the decadal) scales. At the Atmospheric Radiation Measurement Southern Great Plains observatory (ARM SGP) in Lamont, Oklahoma, United States, long-term measurements for PM2.5 speciation (NH4, SO4, NO3, and organic carbon (OC)), NOx, O3, SO2, T, and RH, as well as [CCN0.4] are available. We modify, optimize, and retrain the developed RFRM to make predictions from 19 to 9 of these available predictors. This retrained RFRM (RFRM-ShortVars) shows a reduction in performance due to the unavailability and sparsity of measurements (predictors); it captures the [CCN0.4] variability and magnitude at SGP with ≈67.02 % of the derived values in the good agreement range. This work shows the potential of using the more commonly available measurements of PM2.5 speciation to alleviate the sparsity of CCN number concentrations' measurements.

An Exploratory Approach Using Regression and Machine Learning in the Analysis of Mass Absorption Cross Section of Black Carbon Aerosols: Model Development and Evaluation

Mass absorption cross-section of black carbon (MACBC) describes the absorptive cross-section per unit mass of black carbon, and is, thus, an essential parameter to estimate the radiative forcing of black carbon. Many studies have sought to estimate MACBC from a theoretical perspective, but these studies require the knowledge of a set of aerosol properties, which are difficult and/or labor-intensive to measure. We therefore investigate the ability of seven data analytical approaches (including different multivariate regressions, support vector machine, and neural networks) in predicting MACBC for both ambient and biomass burning measurements. Our model utilizes multi-wavelength light absorption and scattering as well as the aerosol size distributions as input variables to predict MACBC across different wavelengths. We assessed the applicability of the proposed approaches in estimating MACBC using different statistical metrics (such as coefficient of determination (R2), mean square error (MSE), fractional error, and fractional bias). Overall, the approaches used in this study can estimate MACBC appropriately, but the prediction performance varies across approaches and atmospheric environments. Based on an uncertainty evaluation of our models and the empirical and theoretical approaches to predict MACBC, we preliminarily put forth support vector machine (SVM) as a recommended data analytical technique for use. We provide an operational tool built with the approaches presented in this paper to facilitate this procedure for future users.