Meteorological and Air Quality Models for Urban Areas

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.

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

10.21203/rs.3.rs-282971/v1 ◽

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.

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

Environmental Chemistry ◽

10.1071/en13039 ◽

2013 ◽

Vol 10 (3) ◽

pp. 245 ◽

Cited By ~ 4

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.

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

10.5194/gmd-2018-81 ◽

2018 ◽

Cited By ~ 1

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.

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The impact of different nitrous acid sources in the air quality levels of the Iberian Peninsula

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-10-28183-2010 ◽

2010 ◽

Vol 10 (11) ◽

pp. 28183-28230 ◽

Cited By ~ 3

Author(s):

M. Gonçalves ◽

D. Dabdub ◽

W. L. Chang ◽

F. Saiz ◽

O. Jorba ◽

...

Keyword(s):

Air Quality ◽

Iberian Peninsula ◽

Gas Phase ◽

Urban Areas ◽

Nitrous Acid ◽

Quality Models ◽

Chemical Mechanisms ◽

Gas Phase Chemistry ◽

Secondary Pollutants ◽

Phase Chemistry

Abstract. Hydroxyl radical (OH) is a primary oxidant in the atmosphere and affects both gas-phase pollutants and particulate matter levels. Nitrous acid (HONO) acts as an important source of OH in the urban atmosphere. Therefore it is important to account accurately for HONO sources within air quality models in order to predict air pollution dynamics. HONO observations in urban areas are characterized by high concentrations at night and low concentrations around midday. Existing gas-phase chemical mechanisms do not reproduce the observed HONO levels, suggesting a lack of sources, such as direct emissions or heterogeneous reactions. Specific HONO emission rates, heterogeneous chemical mechanisms leading to its formation and related kinetics are still unclear. Therefore, most air quality models consider exclusively gas-phase chemistry related to HONO. This work applies the WRF-ARW/HERMES/CMAQ modeling system to quantify the effect of the addition of HONO sources in the predictability of HONO profiles, and its subsequent effect on secondary pollutants formation (mainly O3 and PM2.5). The modeling episode is based on a 2004 severe summertime pollution event in the Iberian Peninsula, using high resolution of 4 × 4 km2. Two different parameterizations for emissions and the hydrolysis of NO2 on wet surfaces are added as HONO sources in the atmosphere. Emissions have the largest impact on HONO levels, especially in urban areas, where they can contribute from 66% to 94% to the HONO peak concentration. Additionally, in urban environments, NO2 hydrolysis on building and vegetation surfaces contributes up to 30% to the HONO peak. Both, the available surface area and the relative humidity must be included as parameters affecting the NO2 hydrolysis kinetics. As a result, NO2 hydrolysis is negligible on aerosol surfaces, due to the small surface area available for reaction, and it is more effective in producing HONO below high relative humidity conditions. The addition of HONO sources affects the concentration of secondary pollutants. In particular, major changes are produced in the early morning, due to the higher OH release via HONO photolysis. Significant changes in PM2.5 concentrations are predicted, that can be 16% (2.6 μg m−3) higher in the new scenarios. When accounting for HONO sources, nitrate levels increase especially in urban areas and sulfates in areas downwind from conventional power plants in the Iberian Peninsula. Also, O3 peak concentrations are slightly affected (from 0.7 to 4 ppb, 1% to 4.5%). The improvement of the HONO sources representation within air quality models produces changes in O3 peak predictions and significantly affects the reaction pathways leading to aerosols formation. Therefore, HONO sources other than gas-phase chemistry should be accurately included within modeling frameworks.

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Intelligent transport systems - Urban ITS - Air quality management in urban areas

10.3403/30392680 ◽

2019 ◽

Keyword(s):

Air Quality ◽

Quality Management ◽

Urban Areas ◽

Transport Systems ◽

Air Quality Management ◽

Intelligent Transport Systems ◽

Intelligent Transport

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Improving the Protection of Children Against Air Pollution Threats in Romania � The RokidAIR Project Approach and Future Perspectives

Revista de Chimie ◽

10.37358/rc.17.4.5563 ◽

2017 ◽

Vol 68 (4) ◽

pp. 841-846

Author(s):

Hai-Ying Liu ◽

Daniel Dunea ◽

Mihaela Oprea ◽

Tom Savu ◽

Stefania Iordache

Keyword(s):

Air Pollution ◽

Air Quality ◽

Health Effects ◽

Urban Areas ◽

Behavioural Change ◽

Health Assessment ◽

Warning System ◽

Personal Exposure ◽

Mobile Technologies ◽

Quality Maps

This paper presents the approach used to develop the information chain required to reach the objectives of the EEA Grants� RokidAIR project in two Romanian cities i.e., Targoviste and Ploiesti. It describes the PM2.5 monitoring infrastructure and architecture to the web-based GIS platform, the early warning system and the decision support system, and finally, the linking of air pollution to health effects in children. In addition, it shows the analysis performance of the designed system to process the collected time series from various data sources using the benzene concentrations monitored in Ploiesti. Moreover, this paper suggests that biomarkers, mobile technologies, and Citizens� Observatories are potential perspectives to improve data coverage by the provision of near-real-time air quality maps, and provide personal exposure and health assessment results, enabling the citizens� engagement and behavioural change. This paper also addresses new fields in nature-based solutions to improve air quality, and studies on air pollution and its mental health effects in the urban areas of Romania.

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Morpho-Physio-Biochemical Attributes of Roadside Trees as Potential Tools for Biomonitoring of Air Quality and Environmental Health in Urban Areas

Land ◽

10.3390/land10030236 ◽

2021 ◽

Vol 10 (3) ◽

pp. 236

Author(s):

Ha Na You ◽

Myeong Ja Kwak ◽

Sun Mi Je ◽

Jong Kyu Lee ◽

Yea Ji Lim ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Environmental Health ◽

Urban Areas ◽

Biochemical Parameters ◽

Lipid Peroxide ◽

Platanus Occidentalis ◽

Physiological Variables ◽

Sources Of Pollution ◽

Species Specific

Environmental pollution is an important issue in metropolitan areas, and roadside trees are directly affected by various sources of pollution to which they exhibit numerous responses. The aim of the present study was to identify morpho-physio-biochemical attributes of maidenhair tree (Ginkgo biloba L.) and American sycamore (Platanus occidentalis L.) growing under two different air quality conditions (roadside with high air pollution, RH and roadside with low air pollution, RL) and to assess the possibility of using their physiological and biochemical parameters as biomonitoring tools in urban areas. The results showed that the photosynthetic rate, photosynthetic nitrogen-use efficiencies, and photochromic contents were generally low in RH in both G. biloba and P. occidentalis. However, water-use efficiency and leaf temperature showed high values in RH trees. Among biochemical parameters, in G. biloba, the lipid peroxide content was higher in RH than in RL trees, but in P. occidentalis, this content was lower in RH than in RL trees. In both species, physiological activities were low in trees planted in areas with high levels of air pollution, whereas their biochemical and morphological variables showed different responses to air pollution. Thus, we concluded that it is possible to determine species-specific physiological variables affected by regional differences of air pollution in urban areas, and these findings may be helpful for monitoring air quality and environmental health using trees.

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