scholarly journals MOVEIM v1.0: Development of a bottom-up motor vehicular emission inventories for the urban area of Manaus in central Amazon rainforest

2018 ◽  
Author(s):  
Paulo R. Teixeira ◽  
Saulo R. de Freitas ◽  
Francis W. Correia ◽  
Antonio O. Manzi
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Spatial Scales ◽  
Transport Sector ◽  
Emission Inventories ◽  
Monitoring Networks ◽  
Weather Modification ◽  
Vehicular Emission ◽  
Bottom Up ◽  
Quality Models

Abstract. Emissions of gases and particulates in urban areas are associated with a mixture of various sources, both natural and anthropogenic. Understanding and quantifying these emissions is necessary in studies of climate change, local air pollution issues and weather modification. Studies have highlighted that the transport sector is key to closing the world’s emissions gap. Vehicles contribute substantially with the emission of carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), non-methane hydrocarbon (NMHC), particulate matter (PM), methane (CH4), hydrofluorocarbon (HFC) and nitrous oxide (N2O). Several studies show that vehicle emission inventories are an important approach to providing a baseline estimate of on-road emissions in several scales, mainly in urban areas. This approach is essential to areas with incomplete or non-existent monitoring networks as well as for air quality models. Conversely, the direct downscale of global emission inventories in chemical transport and air quality models may not be able to reproduce the observed evolution of atmospheric pollution processes at finer spatial scales. To address this caveat, we developed a bottom-up vehicular emission inventory along the 258 main traffic routes from Manaus, based on local vehicle fleet data and emission factors (EFs). The results show that the light vehicles are responsible for the largest fraction of the pollutants, contributing 2.6, 0.87, 0.32, 0.03, 456 and 0.8 ton/h of CO, NOx, CH4, PM, CO2 and NMHC, respectively. Including the emissions of motorcycles, buses and trucks, our total estimation of the emissions is 4.1, 1.0, 0.37, 0.07, 63.5 and 2.56 ton/h, respectively. We also noted that light vehicles accounted for about 62.8 %, 84.7 %, 87.9 %, 45.1 %, 71.8 %, and 33.9 % and motorcycles in the order of 32.3 %, 6.5 %, 12.1 %, 6.2 %, 14.8 %, 8.7 %, respectively. Nevertheless, we can highlight the bus emissions which are around 35.7 % and 45.3 % for NMHC and PM. Our results indicate a better distribution over the domain reflecting the influences of standard behavior of traffic distribution per vehicle category. Finally, this inventory provides more detailed information to improving the current understanding of how vehicle emissions contribute to the ambient pollutant concentrations in Manaus and their impacts on regional climate changes. This work will also contribute to improved air quality numerical simulations, provide more accurate scenarios for policymakers and regulatory agencies to develop strategies for controlling the vehicular emissions, and, consequently, mitigate associated impacts on local and regional scales of the Amazon ecosystems.

scholarly journals Impact of spatial proxies on the representation of bottom-up emission inventories: A satellite-based analysis

2017 ◽  
Vol 17 (6) ◽  
pp. 4131-4145 ◽  
Author(s):  
Guannan Geng ◽  
Qiang Zhang ◽  
Randall V. Martin ◽  
Jintai Lin ◽  
Hong Huo ◽  
...  
Keyword(s):  
Population Density ◽  
Rural Areas ◽  
Urban Areas ◽  
Model Performance ◽  
Road Transport ◽  
Industrial Sector ◽  
Transport Sector ◽  
Emission Inventories ◽  
Bottom Up ◽  
The Impact

Abstract. Spatial proxies used in bottom-up emission inventories to derive the spatial distributions of emissions are usually empirical and involve additional levels of uncertainty. Although uncertainties in current emission inventories have been discussed extensively, uncertainties resulting from improper spatial proxies have rarely been evaluated. In this work, we investigate the impact of spatial proxies on the representation of gridded emissions by comparing six gridded NOx emission datasets over China developed from the same magnitude of emissions and different spatial proxies. GEOS-Chem-modeled tropospheric NO2 vertical columns simulated from different gridded emission inventories are compared with satellite-based columns. The results show that differences between modeled and satellite-based NO2 vertical columns are sensitive to the spatial proxies used in the gridded emission inventories. The total population density is less suitable for allocating NOx emissions than nighttime light data because population density tends to allocate more emissions to rural areas. Determining the exact locations of large emission sources could significantly strengthen the correlation between modeled and observed NO2 vertical columns. Using vehicle population and an updated road network for the on-road transport sector could substantially enhance urban emissions and improve the model performance. When further applying industrial gross domestic product (IGDP) values for the industrial sector, modeled NO2 vertical columns could better capture pollution hotspots in urban areas and exhibit the best performance of the six cases compared to satellite-based NO2 vertical columns (slope  =  1.01 and R2 = 0. 85). This analysis provides a framework for information from satellite observations to inform bottom-up inventory development. In the future, more effort should be devoted to the representation of spatial proxies to improve spatial patterns in bottom-up emission inventories.

scholarly journals Estimating lockdown-induced European NO<sub>2</sub> changes using satellite and surface observations and air quality models

2021 ◽  
Vol 21 (9) ◽  
pp. 7373-7394
Author(s):  
Jérôme Barré ◽  
Hervé Petetin ◽  
Augustin Colette ◽  
Marc Guevara ◽  
Vincent-Henri Peuch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Urban Areas ◽  
Gradient Boosting ◽  
Quality Models ◽  
Pollution Levels ◽  
Surface Station ◽  
Concentration Changes ◽  
Monitoring Service ◽  
No2 Pollution

Abstract. This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by COVID-19 lockdowns using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5p satellite, surface site measurements, and simulations from the Copernicus Atmosphere Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of changes in atmospheric NO2 concentrations have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite-estimated NO2 column changes with weather-normalized surface NO2 concentration changes and the CAMS regional ensemble, composed of 11 models, using recently published estimates of emission reductions induced by the lockdown. All estimates show similar NO2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Average reduction estimates based on either satellite observations (−23 %), surface stations (−43 %), or models (−32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (−37 %), pointing out the importance of the variability in time of such estimates. Observation-based machine learning estimates show a stronger temporal variability than model-based estimates.

scholarly journals Assessing a low-cost methane sensor quantification system for use in complex rural and urban environments

2018 ◽  
Author(s):  
Ashley Collier-Oxandale ◽  
Michael P. Hannigan ◽  
Joanna Gordon Casey ◽  
Ricardo Piedrahita ◽  
John Ortega ◽  
...  
Keyword(s):  
Air Quality ◽  
Los Angeles ◽  
Oil And Gas ◽  
Spatial Scales ◽  
Low Cost ◽  
Complex Mixture ◽  
Urban Environments ◽  
Monitoring Networks ◽  
Linear Calibration ◽  
Spatial And Temporal Scales

Abstract. Low-cost sensors have the potential to facilitate the exploration of air quality issues on new temporal and spatial scales. Here we evaluate a low-cost sensor quantification system for methane through its use in two different deployments. The first, a one-month deployment along the Colorado Front Range includes sites near active oil and gas operations in the Denver-Julesberg basin. The second deployment in an urban Los Angeles neighborhood, an subject to complex mixture of air pollution sources including oil operations. Given its role as a potent greenhouse gas, new low-cost methods for detecting and monitoring methane may aid in protecting human and environmental health. In this paper, we assess a number of linear calibration models to convert raw sensor signals into ppm concentration values. We also examine different choices that can be made during calibration and data processing, and explore cross-sensitivities that impact this sensor type. The results illustrate the accuracy of the Figaro TGS 2600 sensor when methane is quantified from raw signals using the techniques described. The results also demonstrate the value of these tools for examining air quality trends and events on small spatial and temporal scales as well as their ability to characterize an area – highlighting their potential to provide preliminary data that can inform more targeted measurements or supplement existing monitoring networks.

scholarly journals Air Quality Modeling for Sustainable Clean Environment Using ANFIS and Machine Learning Approaches

2021 ◽  
Author(s):  
Osman Taylan ◽  
Abdulaziz Alkabaa ◽  
Mohammed Alamoudi ◽  
Abdulrahman Basahel ◽  
Mohammad Balubaid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Urban Areas ◽  
Back Propagation ◽  
Fuzzy Model ◽  
Nonlinear Least Squares ◽  
Optimization Method ◽  
Learning Approaches ◽  
Quality Models ◽  
Neuro Fuzzy

Abstract Air quality monitoring and assessment are essential issues for sustainable environmental protection. The monitoring process is composed of data collection, evaluation, and decision making. Several important pollution factors, such as SO2, CO, PM10, O3, NOx, H2S, location, and many others, have detrimental effects on air quality. Air quality cannot be precisely recorded and measured due to the total effect of pollutants that usually cannot be collectively prescribed by a numerical value. Therefore, evolution is required to take into account the complex, poorly defined air quality problems in which several naive and noble modeling approaches are used to evaluate and solve. In this study, hybrid data-driven machine learning, and neuro-fuzzy methods are integrated for estimating the air quality in the urban area for public health concerns. 1771 data are collected during three years for each pollution factor, starting from June 1, 2016, till September 30, 2019. The Back-Propagation Multi-Layer Perceptron (BPMLP) algorithm was employed with the steepest descent approach to reduce the mean square error for training the algorithm of the neuro-fuzzy model. Levenberg-Marquardt (LM) approach was also employed as an optimization method with Artificial neural networks (ANNs) for solving nonlinear least-squares problems in this study. These approaches were evaluated by fuzzy quality charts and compared statistically with the US-EPA air quality standards. Due to the effectiveness and robustness of soft computing intelligent models, the public's early warning will be possible for avoiding the harmful effects of pollution inside the urban areas, which may reduce respiratory and cardiovascular mortalities. Consequently, the stability of air quality models was correlated with the absolute air quality index. The findings showed remarkable performance of ANFIS and ANN-based Air Quality models for High dimensional data assessment.

Evaluation of aromatic oxidation reactions in seven chemical mechanisms with an outdoor chamber

2013 ◽  
Vol 10 (3) ◽  
pp. 245 ◽  
Author(s):  
Harshal M. Parikh ◽  
Harvey E. Jeffries ◽  
Ken G. Sexton ◽  
Deborah J. Luecken ◽  
Richard M. Kamens ◽  
...  
Keyword(s):  
Air Quality ◽  
Aromatic Ring ◽  
Aromatic Hydrocarbons ◽  
Urban Areas ◽  
Ring Opening ◽  
Chamber Wall ◽  
Oxidation Reactions ◽  
Quality Models ◽  
Chemical Mechanisms ◽  
Wide Range

Environmental context Regulatory air quality models used to develop strategies to reduce ozone and other pollutants must be able to accurately predict ozone produced from aromatic hydrocarbons. In urban areas, major sources of aromatic hydrocarbons are gasoline and diesel-powered vehicles. Our findings show that the representation of aromatic hydrocarbon chemistry in air quality models is an area of high uncertainty Abstract Simulations using seven chemical mechanisms are intercompared against O3, NOx and hydrocarbon data from photooxidation experiments conducted at the University of North Carolina outdoor smog chamber. The mechanisms include CB4–2002, CB05, CB05-TU, a CB05 variant with semi-explicit aromatic chemistry (CB05RMK), SAPRC07, CS07 and MCMv3.1. The experiments include aromatics, unsaturated dicarbonyls and volatile organic compound (VOC) mixtures representing a wide range of urban environments with relevant hydrocarbon species. In chamber simulations the sunlight is characterised using new solar radiation modelling software. A new heterogeneous chamber wall mechanism is also presented with revised chamber wall chemical processes. Simulations from all mechanisms, except MCMv3.1, show median peak O3 concentration relative errors of less than 25% for both aromatic and VOC mixture experiments. Although MCMv3.1 largely overpredicts peak O3 levels, it performs relatively better in predicting the peak NO2 concentration. For aromatic experiments, all mechanisms except CB4–2002, largely underpredict the NO–NO2 crossover time and over-predict both the absolute NO degradation slope and the slope of NO2 concentration rise. This suggests a major problem of a faster and earlier NO to NO2 oxidation rate across all the newer mechanisms. Results from individual aromatic and unsaturated dicarbonyl experiments illustrate the unique photooxidation chemistry and O3 production of several aromatic ring-opening products. The representation of these products as a single mechanism species in CB4–2002, CB05 and CB05-TU is not adequate to capture the O3 temporal profile. In summary, future updates to chemical mechanisms should focus on the chemistry of aromatic ring-opening products.

scholarly journals Application of a Machine Learning Methodology for Data Implementation

2020 ◽  
Vol 4 (1) ◽  
pp. 11
Author(s):  
Chris G. Tzanis ◽  
Anastasios Alimissis ◽  
Ioannis Koutsogiannis
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Urban Areas ◽  
Spatial Interpolation ◽  
Missing Values ◽  
Ambient Air ◽  
Monitoring Networks ◽  
Policy Makers ◽  
Environmental Statistics ◽  
Quality Maps

An important aspect in environmental sciences is the study of air quality, using statistical methods (environmental statistics) which utilize large datasets of climatic parameters. The air quality monitoring networks that operate in urban areas provide data on the most important pollutants, which via environmental statistics can be used for the development of continuous surfaces of pollutants’ concentrations. Generating ambient air quality maps can help guide policy makers and researchers to formulate measures to minimize the adverse effects. The information needed for a mapping application can be obtained by employing spatial interpolation methods to the available data, for generating estimations of air quality distributions. This study used point monitoring data from the network of stations that operates in Athens. A machine learning scheme was applied as a method to spatially estimate pollutants’ concentrations and the results could be effectively used to implement missing values and provide representative data for statistical analyses purposes.

Assessment of discrepancies between bottom-up and regional emission inventories in Norwegian urban areas

2017 ◽  
Vol 154 ◽  
pp. 285-296 ◽  
Author(s):  
Susana López-Aparicio ◽  
Marc Guevara ◽  
Philippe Thunis ◽  
Kees Cuvelier ◽  
Leonor Tarrasón
Keyword(s):  
Urban Areas ◽  
Emission Inventories ◽  
Bottom Up

scholarly journals Evaluation of modeling NO<sub>2</sub> concentrations driven by satellite-derived and bottom-up emission inventories using in situ measurements over China

2018 ◽  
Vol 18 (6) ◽  
pp. 4171-4186 ◽  
Author(s):  
Fei Liu ◽  
Ronald J. van der A ◽  
Henk Eskes ◽  
Jieying Ding ◽  
Bas Mijling
Keyword(s):  
Spatial Correlation ◽  
Rural Areas ◽  
Urban Areas ◽  
Transport Model ◽  
Chemical Transport ◽  
In Situ Measurements ◽  
Nox Emissions ◽  
Emission Inventories ◽  
Bottom Up

Abstract. Chemical transport models together with emission inventories are widely used to simulate NO2 concentrations over China, but validation of the simulations with in situ measurements has been extremely limited. Here we use ground measurements obtained from the air quality monitoring network recently developed by the Ministry of Environmental Protection of China to validate modeling surface NO2 concentrations from the CHIMERE regional chemical transport model driven by the satellite-derived DECSO and the bottom-up MIX emission inventories. We applied a correction factor to the observations to account for the interferences of other oxidized nitrogen compounds (NOz), based on the modeled ratio of NO2 to NOz. The model accurately reproduces the spatial variability in NO2 from in situ measurements, with a spatial correlation coefficient of over 0.7 for simulations based on both inventories. A negative and positive bias is found for the simulation with the DECSO (slope  =  0.74 and 0.64 for the daily mean and daytime only) and the MIX (slope  =  1.3 and 1.1) inventories, respectively, suggesting an underestimation and overestimation of NOx emissions from corresponding inventories. The bias between observed and modeled concentrations is reduced, with the slope dropping from 1.3 to 1.0 when the spatial distribution of NOx emissions in the DECSO inventory is applied as the spatial proxy for the MIX inventory, which suggests an improvement of the distribution of emissions between urban and suburban or rural areas in the DECSO inventory compared to that used in the bottom-up inventory. A rough estimate indicates that the observed concentrations, from sites predominantly placed in the populated urban areas, may be 10–40 % higher than the corresponding model grid cell mean. This reduces the estimate of the negative bias of the DECSO-based simulation to the range of −30 to 0 % on average and more firmly establishes that the MIX inventory is biased high over major cities. The performance of the model is comparable over seasons, with a slightly worse spatial correlation in summer due to the difficulties in resolving the more active NOx photochemistry and larger concentration gradients in summer by the model. In addition, the model well captures the daytime diurnal cycle but shows more significant disagreement between simulations and measurements during nighttime, which likely produces a positive model bias of about 15 % in the daily mean concentrations. This is most likely related to the uncertainty in vertical mixing in the model at night.

scholarly journals Evaluation of modelling NO<sub>2</sub> concentrations driven by satellite-derived and bottom-up emission inventories using in-situ measurements over China

2017 ◽  
Author(s):  
Fei Liu ◽  
Ronald J. van der A ◽  
Henk Eskes ◽  
Jieying Ding ◽  
Bas Mijling
Keyword(s):  
Spatial Correlation ◽  
Rural Areas ◽  
Urban Areas ◽  
Transport Model ◽  
Chemical Transport ◽  
In Situ Measurements ◽  
Nox Emissions ◽  
Emission Inventories ◽  
Bottom Up

Abstract. Chemical transport models together with emission inventories are widely used to simulate NO2 concentrations over China, but validation of the simulations with in situ measurements has been extremely limited. Here we use ground measurements obtained from the air quality monitoring network recently developed by the Ministry of Environmental Protection of China to validate modelling surface NO2 concentrations from the CHIMERE regional chemical-transport model driven by the satellite-derived DECSO and the bottom-up MIX emission inventories. We applied a correction factor to the observations to account for the interferences of other oxidized nitrogen compounds (NOz), based on the modelled ratio of NO2 to NOz. The model accurately reproduces the spatial variability of NO2 from in-situ measurements, with a spatial correlation coefficient of over 0.7 for simulations based on both inventories. A negative and positive bias is found for the simulation with the DECSO (slope = 0.74/0.64 for the daily-mean/daytime only) and the MIX (slope = 1.3/1.1) inventory respectively, suggesting an underestimation and overestimation of NOx emissions from corresponding inventories. The bias between observed and modelled concentrations is reduced with the slope dropping from 1.3 to 1.0 when the spatial distribution of NOx emissions in the DECSO inventory is applied as the spatial proxy for the MIX inventory, which suggests an improvement of the distribution of emissions between urban and suburban/rural areas in the DECSO inventory compared to that used in the bottom-up inventory. A rough estimate indicates that the observed concentrations, from sites predominantly placed in the populated urban areas, may be 10–40 % higher than the corresponding model grid-cell mean. This reduces the estimate of the negative bias of the DECSO based simulation to the range of −30 % to 0 % on average, and establishes more firmly that the MIX inventory is biased high over major cities. The performance of the model is comparable over seasons, with a slightly worse spatial correlation in summer, due to the difficulties in resolving the more active NOx photochemistry and larger concentration gradients in summer by the model. In addition, the model well captures the daytime diurnal cycle, but shows more significant disagreement between simulations and measurements during night time, which likely produces a positive model bias of about 15 % in the daily mean concentrations. This is most likely related to the uncertainty in vertical mixing in the model at night.

scholarly journals A GLOBAL PERSPECTIVE OF VEHICULAR EMISSION CONTROL POLICY AND PRACTICES: AN INTERFACE WITH KATHMANDU VALLEY CASE, NEPAL

2019 ◽  
Vol 23 (1) ◽  
pp. 76-80
Author(s):  
Bhupendra Das ◽  
Prakash V. Bhave ◽  
Siva Praveen Puppala ◽  
Rejina M. Byanju
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Emission Control ◽  
Control Policy ◽  
Developed Countries ◽  
Global Perspective ◽  
Transport Sector ◽  
Kathmandu Valley ◽  
Vehicular Emission ◽  
The Developed Countries

Transport sector is growing most rapidly around the world in line with the urban and socio-economic growth, which is contributing to severe air pollution. Air pollution has been of much concern mainly due to air quality, human exposure, public health, climate change, and visibility reduction. At present, in the media and policy arena, significant attention is given to the transport air pollution and its effect. Although most of the developed countries established vehicular emission control practices, it is very primitive in the developing countries including Nepal. This paper highlights global policies/legislations that have been practiced for emissions control from high emitting vehicles based on the available literature. The insights and lessons based information presented in this paper will add value to the policy makers for creating strong policy packages of air quality management for Kathmandu valley including other parts of Nepal.

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