scholarly journals Spatial and temporal variability of ozone along the Colorado Front Range occurring over 2 days with contrasting wind flow

Elem Sci Anth ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Lisa S. Darby ◽  
Christoph J. Senff ◽  
Raul J. Alvarez II ◽  
Robert M. Banta ◽  
Laura Bianco ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Air Quality ◽  
Rocky Mountains ◽  
Urban Areas ◽  
High Elevation ◽  
Quality Data ◽  
Spatial And Temporal Variability ◽  
Front Range ◽  
Monitoring Networks ◽  
Wilderness Areas

Transport of pollution into pristine wilderness areas is of concern for both federal and state agencies. Assessing such transport in complex terrain is a challenge when relying solely on data from standard federal or state air quality monitoring networks because of the sparsity of network monitors beyond urban areas. During the Front Range air quality study, conducted in the summer of 2008 in the vicinity of Denver, CO, research-grade surface air quality data, vertical wind profiles and mixing heights obtained by radar wind profilers, and ozone profile data obtained by an airborne ozone differential absorption lidar augmented the local regulatory monitoring networks. Measurements from this study were taken on 2 successive days at the end of July 2008. On the first day, the prevailing winds were downslope westerly, advecting pollution to the east of the Front Range metropolitan areas. On this day, chemistry measurements at the mountain and foothills surface stations showed seasonal background ozone levels of approximately 55–68 ppbv (nmol mol–1 by volume). The next day, upslope winds prevailed, advecting pollution from the Plains into the Rocky Mountains and across the Continental Divide. Mountain stations measured ozone values greater than 90 ppbv, comparable to, or greater than, nearby urban measurements. The measurements show the progression of the ozone-enriched air into the mountains and tie the westward intrusion into high-elevation mountain sites to the growth of the afternoon boundary layer. Thus, under deep upslope flow conditions, ozone-enriched air can be advected into wilderness areas of the Rocky Mountains. Our findings highlight a process that is likely to be an important ozone transport mechanism in mountainous terrain adjacent to ozone source areas when the right circumstances come together, namely a deep layer of light winds toward a mountain barrier coincident with a deep regional boundary layer.

Temporal patterns and trends of surface ozone concentrations over Portugal

2021 ◽  
Author(s):  
Carla Gama ◽  
Alexandra Monteiro ◽  
Myriam Lopes ◽  
Ana Isabel Miranda
Keyword(s):  
Air Quality ◽  
Iberian Peninsula ◽  
Human Health ◽  
Temporal Variability ◽  
Urban Areas ◽  
Surface Ozone ◽  
Quality Data ◽  
Ozone Concentrations ◽  
Long Term Study

<p>Tropospheric ozone (O<sub>3</sub>) is a critical pollutant over the Mediterranean countries, including Portugal, due to systematic exceedances to the thresholds for the protection of human health. Due to the location of Portugal, on the Atlantic coast at the south-west point of Europe, the observed O<sub>3</sub> concentrations are very much influenced not only by local and regional production but also by northern mid-latitudes background concentrations. Ozone trends in the Iberian Peninsula were previously analysed by Monteiro et al. (2012), based on 10-years of O<sub>3</sub> observations. Nevertheless, only two of the eleven background monitoring stations analysed in that study are located in Portugal and these two stations are located in Porto and Lisbon urban areas. Although during pollution events O<sub>3</sub> levels in urban areas may be high enough to affect human health, the highest concentrations are found in rural locations downwind from the urban and industrialized areas, rather than in cities. This happens because close to the sources (e.g., in urban areas) freshly emitted NO locally scavenges O<sub>3</sub>. A long-term study of the spatial and temporal variability and trends of the ozone concentrations over Portugal is missing, aiming to answer the following questions:</p><p>-           What is the temporal variability of ozone concentrations?</p><p>-           Which trends can we find in observations?</p><p>-           How were the ozone spring maxima concentrations affected by the COVID-19 lockdown during spring 2020?</p><p>In this presentation, these questions will be answered based on the statistical analysis of O<sub>3</sub> concentrations recorded within the national air quality monitoring network between 2005 and 2020 (16 years). The variability of the surface ozone concentrations over Portugal, on the timescales from diurnal to annual, will be presented and discussed, taking into account the physical and chemical processes that control that variability. Using the TheilSen function from the OpenAir package for R (Carslaw and Ropkins 2012), which quantifies monotonic trends and calculates the associated p-value through bootstrap simulations, O<sub>3</sub> concentration long-term trends will be estimated for the different regions and environments (e.g., rural, urban).  Moreover, taking advantage of the unique situation provided by the COVID-19 lockdown during spring 2020, when the government imposed mandatory confinement and citizens movement restriction, leading to a reduction in traffic-related atmospheric emissions, the role of these emissions on ozone levels during the spring period will be studied and presented.</p><p> </p><p>Carslaw and Ropkins, 2012. Openair—an R package for air quality data analysis. Environ. Model. Softw. 27-28,52-61. https://doi.org/10.1016/j.envsoft.2011.09.008</p><p>Monteiro et al., 2012. Trends in ozone concentrations in the Iberian Peninsula by quantile regression and clustering. Atmos. Environ. 56, 184-193. https://doi.org/10.1016/j.atmosenv.2012.03.069</p>

scholarly journals Air Pollution Prediction Using Long Short-Term Memory (LSTM) and Deep Autoencoder (DAE) Models

2020 ◽  
Vol 12 (6) ◽  
pp. 2570 ◽  
Author(s):  
Thanongsak Xayasouk ◽  
HwaMin Lee ◽  
Giyeol Lee
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Short Term Memory ◽  
Batch Size ◽  
Quality Data ◽  
Short Term ◽  
Optimal Learning ◽  
Term Memory ◽  
Batch Sizes ◽  
Long Short Term Memory

Many countries worldwide have poor air quality due to the emission of particulate matter (i.e., PM10 and PM2.5), which has led to concerns about human health impacts in urban areas. In this study, we developed models to predict fine PM concentrations using long short-term memory (LSTM) and deep autoencoder (DAE) methods, and compared the model results in terms of root mean square error (RMSE). We applied the models to hourly air quality data from 25 stations in Seoul, South Korea, for the period from 1 January 2015, to 31 December 2018. Fine PM concentrations were predicted for the 10 days following this period, at an optimal learning rate of 0.01 for 100 epochs with batch sizes of 32 for LSTM model, and DAEs model performed best with batch size 64. The proposed models effectively predicted fine PM concentrations, with the LSTM model showing slightly better performance. With our forecasting model, it is possible to give reliable fine dust prediction information for the area where the user is located.

scholarly journals CO<sub>2</sub> dispersion modelling over Paris region within the CO<sub>2</sub>-MEGAPARIS project

2013 ◽  
Vol 13 (9) ◽  
pp. 4941-4961 ◽  
Author(s):  
C. Lac ◽  
R. P. Donnelly ◽  
V. Masson ◽  
S. Pal ◽  
S. Riette ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Temporal Variability ◽  
Urban Areas ◽  
Onset Time ◽  
Urban Canopy ◽  
Horizontal Resolution ◽  
Spatial And Temporal Variability ◽  
Good Representation ◽  
Mixing Ratios ◽  
Urban Rural

Abstract. Accurate simulation of the spatial and temporal variability of tracer mixing ratios over urban areas is a challenging and interesting task needed to be performed in order to utilise CO2 measurements in an atmospheric inverse framework and to better estimate regional CO2 fluxes. This study investigates the ability of a high-resolution model to simulate meteorological and CO2 fields around Paris agglomeration during the March field campaign of the CO2-MEGAPARIS project. The mesoscale atmospheric model Meso-NH, running at 2 km horizontal resolution, is coupled with the Town Energy Balance (TEB) urban canopy scheme and with the Interactions between Soil, Biosphere and Atmosphere CO2-reactive (ISBA-A-gs) surface scheme, allowing a full interaction of CO2 modelling between the surface and the atmosphere. Statistical scores show a good representation of the urban heat island (UHI) with stronger urban–rural contrasts on temperature at night than during the day by up to 7 °C. Boundary layer heights (BLH) have been evaluated on urban, suburban and rural sites during the campaign, and also on a suburban site over 1 yr. The diurnal cycles of the BLH are well captured, especially the onset time of the BLH increase and its growth rate in the morning, which are essential for tall tower CO2 observatories. The main discrepancy is a small negative bias over urban and suburban sites during nighttime (respectively 45 m and 5 m), leading to a few overestimations of nocturnal CO2 mixing ratios at suburban sites and a bias of +5 ppm. The diurnal CO2 cycle is generally well captured for all the sites. At the Eiffel tower, the observed spikes of CO2 maxima occur every morning exactly at the time at which the atmospheric boundary layer (ABL) growth reaches the measurement height. At suburban ground stations, CO2 measurements exhibit maxima at the beginning and at the end of each night, when the ABL is fully contracted, with a strong spatio-temporal variability. A sensitivity test without urban parameterisation removes the UHI and underpredicts nighttime BLH over urban and suburban sites, leading to large overestimation of nocturnal CO2 mixing ratio at the suburban sites (bias of +17 ppm). The agreement between observation and prediction for BLH and CO2 concentrations and urban–rural increments, both day and night, demonstrates the potential of using the urban mesoscale system in the context of inverse modelling

A Cluster Analysis of PM2.5 Using CMAQ Model Results for Representativeness of Air Quality Monitoring Networks in Busan, Korea

2020 ◽  
Author(s):  
Woo-Sik Jung ◽  
Woo-Gon Do
Keyword(s):  
Air Pollution ◽  
Cluster Analysis ◽  
Air Quality ◽  
Monitoring Network ◽  
Hierarchical Cluster ◽  
Quality Monitoring ◽  
Quality Data ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
Monitoring Stations

&lt;p&gt;&lt;strong&gt;With increasing interest in air pollution, the installation of air quality monitoring networks for regular measurement is considered a very important task in many countries. However, operation of air quality monitoring networks requires much time and money. Therefore, the representativeness of the locations of air quality monitoring networks is an important issue that has been studied by many groups worldwide. Most such studies are based on statistical analysis or the use of geographic information systems (GIS) in existing air quality monitoring network data. These methods are useful for identifying the representativeness of existing measuring networks, but they cannot verify the need to add new monitoring stations. With the development of computer technology, numerical air quality models such as CMAQ have become increasingly important in analyzing and diagnosing air pollution. In this study, PM2.5 distributions in Busan were reproduced with 1-km grid spacing by the CMAQ model. The model results reflected actual PM2.5 changes relatively well. A cluster analysis, which is a statistical method that groups similar objects together, was then applied to the hourly PM2.5 concentration for all grids in the model domain. Similarities and differences between objects can be measured in several ways. K-means clustering uses a non-hierarchical cluster analysis method featuring an advantageously low calculation time for the fast processing of large amounts of data. K-means clustering was highly prevalent in existing studies that grouped air quality data according to the same characteristics. As a result of the cluster analysis, PM2.5 pollution in Busan was successfully divided into groups with the same concentration change characteristics. Finally, the redundancy of the monitoring stations and the need for additional sites were analyzed by comparing the clusters of PM2.5 with the locations of the air quality monitoring networks currently in operation.&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2017R1D1A3B03036152).&lt;/strong&gt;&lt;/p&gt;

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.

scholarly journals Vayu: An Open-Source Toolbox for Visualization and Analysis of Crowd-Sourced Sensor Data

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7726
Author(s):  
Sachit Mahajan
Keyword(s):  
Air Quality ◽  
Open Source ◽  
Time Series Data ◽  
Sensor Data ◽  
Quality Data ◽  
Sensor Technology ◽  
Data Conversion ◽  
Series Data ◽  
Monitoring Networks ◽  
Systematic Analysis

Recent advances in sensor technology and the availability of low-cost and low-power sensors have changed the air quality monitoring paradigm. These sensors are being widely used by scientists and citizens for monitoring air quality at finer spatial-temporal resolution. Such practices are opening up opportunities to enhance the traditional monitoring networks, but at the same time, these sensors are producing large data sets that can become overwhelming and challenging when it comes to the scientific tools and skills required to analyze the data. To address this challenge, an open-source, robust, and cross-platform sensor data analysis toolbox called Vayu is developed that allows researchers and citizens to do detailed and reproducible analyses of air quality data. Vayu combines the power of visualization and statistical analysis using a simple and intuitive graphical user interface. Additionally, it offers a comprehensive set of tools for systematic analysis such as data conversion, interpolation, aggregation, and prediction. Even though Vayu was developed with air quality research in mind, it can be used to analyze different kinds of time-series data.

scholarly journals Intra–Community Scale Variability of Air Quality in the Center of a Megacity in South Korea: A High-Density Cost-Effective Sensor Network

2021 ◽  
Vol 11 (19) ◽  
pp. 9105
Author(s):  
Yongmi Park ◽  
Ho-Seon Park ◽  
Subin Han ◽  
Kyucheol Hwang ◽  
Seunghyun Lee ◽  
...  
Keyword(s):  
Air Quality ◽  
South Korea ◽  
Sensor Network ◽  
Temporal Variability ◽  
Urban Areas ◽  
Pearson Correlation ◽  
Urban Canopy ◽  
Air Pollutant ◽  
Sensor Nodes ◽  
Spatial And Temporal Variability

To investigate the spatial and temporal variability of air quality (CO, NO2, O3, and PM2.5) with a high spatial resolution in various adjacent micro-environments, 30 sets of sensor-nodes were deployed within an 800 × 800 m monitoring domain in the center of the largest megacity (Seoul) in South Korea. The sensor network was operated in summer and winter. The daily variation in air pollutant concentrations revealed a similar trend, with discernible concentration differences among monitoring sub-sites and a government-operated air quality monitoring station. These differences in pollutant levels (except PM2.5) among the sub-sites were pronounced in the daytime with high volumes of traffic. The coefficient of divergence and Pearson correlation coefficient showed that spatial and temporal variability was more significant in summer than winter. Ozone displayed the greatest spatial variability, with little temporal variability among the sub-sites and a negative correlation with NO2, implying that ozone concentrations were primarily determined by vehicular NOX emissions due to NO titration effects under the urban canopy. The PM2.5 concentration displayed homogeneous spatial and temporal distributions over the entire monitoring period, implying that PM2.5 monitoring with at least a 1 × 1 km resolution is sufficient to examine the spatial and temporal heterogeneity in urban areas.

scholarly journals Novel Air Pollution Measurement System Based on Ethereum Blockchain

2020 ◽  
Vol 9 (4) ◽  
pp. 49
Author(s):  
Daniele Sofia ◽  
Nicoletta Lotrecchiano ◽  
Paolo Trucillo ◽  
Aristide Giuliano ◽  
Luigi Terrone
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Query Language ◽  
Environmental Data ◽  
Quality Monitoring ◽  
Traffic Information ◽  
Quality Data ◽  
Air Quality Monitoring ◽  
Monitoring Networks

The need to protect sensitive data is growing, and environmental data are now considered sensitive. The application of last-generation procedures such as blockchains coupled with the implementation of new air quality monitoring technology allows the data protection and validation. In this work, the use of a blockchain applied to air pollution data is proposed. A blockchain procedure has been designed and tested. An Internet of Things (IoT)-based sensor network provides air quality data in terms of particulate matter of two different diameters, particulate matter (PM)10 and PM2.5, volatile organic compounds (VOC), and nitrogen dioxide (NO2) concentrations. The dataset also includes meteorological parameters and vehicular traffic information. This work foresees that the data, recovered from traditional Not Structured Query Language (NoSQL) database, and organized according to some specifications, are sent to the Ethereum blockchain daily automatically and with the possibility to choose the period of interest manually. There was also the development of a transaction management and recovery system aimed at retrieving data, formatting it according to the specifications and organizing it into files of various formats. The blockchain procedure has therefore been used to track data provided by air quality monitoring networks unequivocally.

scholarly journals Effects of fireworks on particulate matter concentration in a narrow valley: the case of the Medellín metropolitan area

2019 ◽  
Vol 192 (1) ◽  
Author(s):  
Carlos D. Hoyos ◽  
Laura Herrera-Mejía ◽  
Natalia Roldán-Henao ◽  
Alejandra Isaza
Keyword(s):  
Boundary Layer ◽  
Particulate Matter ◽  
Air Quality ◽  
Metropolitan Area ◽  
Fine Particles ◽  
Microwave Radiometer ◽  
Potential Temperature ◽  
Quality Data ◽  
Convective Boundary ◽  
Concentration Changes

AbstractThe extensive use of fireworks generates large amounts of pollutants, deteriorating air quality and potentially causing adverse health impacts. In Medellín and its metropolitan area, although fireworks are banned during December, their use is widespread during the Christmas season, particularly during the midnight of November 30 (La Alborada) and New Year’s Eve (NYE). It is therefore essential to assess the effects of these celebrations on air quality in the region. Air-quality data from the official monitoring network and a low-cost particulate matter (PM) citizen science project, backscattering intensity (BI) retrievals from a ceilometer network, potential temperature from a microwave radiometer, and information from a radar wind profiler provide an excellent platform to study the spatio-temporal distribution of contaminants resulting from the La Alborada and NYE celebrations. Substantial increases in PM2.5 and PM10 mass concentrations due to La Alborada and NYE, ranging in some cases from 50 to 100 μgm−3, are observed in the Aburrá Valley and particularly in the densely populated communes of Medellín, with most concentration changes corresponding to ultrafine and fine particles. The PM increments resulting from fireworks show almost no increase in the net amount of black carbon in the atmosphere. Ceilometer BI profiles show a substantial change immediately after the La Alborada and NYE midnights, confined to the atmospheric boundary layer (ABL). Strong thermal inversions lead to fairly homogeneous increments in BI within the ABL, lasting until the onset of the convective boundary layer. In contrast, weak thermal inversions lead to rapid dispersion of aerosols, allowing them to episodically escape above the ABL.

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.

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