scholarly journals Characterization of particulate matter in iron ore mining region of Itabira, Minas Gerais, Brazil

2021 ◽  
Author(s):  
Ana Carolina Vasques Freitas ◽  
Rose-Marie Belardi ◽  
Henrique de Melo Jorge Barbosa
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Fine Particulate Matter ◽  
Monitoring Network ◽  
Principal Component ◽  
Opencast Mining ◽  
Residential Areas ◽  
Emission Data ◽  
Particulate Matter Emission ◽  
The City

Itabira has in its territory the largest complex of opencast mining in the world, which is located close to residential areas of the city. The air quality-monitoring network installed in the city is the main source of particulate matter emission data. However, these air quality stations only cover the areas near the mines and does not measure fine particulate matter. Thus, a first field campaign was carried out to characterize the particulate matter in the city and to compare the Hi-Vol data from air quality stations with the dichotomous air sampler data. Results of trajectories cluster analysis showed a long-range transport of aerosols during the sampling days from northeast (84% of the trajectories), east-southeast (12%) and south-southwest (3%) directions. Regarding to the meteorological conditions during the sampling days, negative correlations were seen between coarse particulate matter from mostly air quality stations and all meteorological parameters (but temperature). Results of the X-ray fluorescence and principal component analyses showed that the main trace elements in the coarse and fine modes are Iron and Sulfur, associated with emissions from mining activities, air mass transport from regional iron and steelmaking industry activities, vehicle emissions, local and regional biomass burning and natural biogenic emissions. This work represents the first assessment of source apportionment done in the city. Comparisons with other studies for some Brazilian larger cities showed that Itabira has comparable contributions of sulfur, iron and elements, such as copper, selenium, chromium, nickel, vanadium and lead.

Hybrid Model for Prediction of Carbon Monoxide and Fine Particulate Matter Concentrations near a Road Intersection

2015 ◽  
Vol 2503 (1) ◽  
pp. 29-38 ◽  
Author(s):  
Zhanyong Wang ◽  
Hong-Di He ◽  
Feng Lu ◽  
Qing-Chang Lu ◽  
Zhong-Ren Peng
Keyword(s):  
Neural Network ◽  
Carbon Monoxide ◽  
Particulate Matter ◽  
Air Quality ◽  
Hybrid Model ◽  
Urban Areas ◽  
Fine Particulate Matter ◽  
Principal Component ◽  
Fine Particulate ◽  
Proposed Model

Air quality time series near road intersections consist of complex linear and nonlinear patterns and are difficult to forecast. The backpropagation neural network (BPNN) has been applied for air quality forecasting in urban areas, but it has limited accuracy because of the inability to predict extreme events. This study proposed a novel hybrid model called GAWNN that combines a genetic algorithm and a wavelet neural network to improve forecast accuracy. The proposed model was examined through predicting the carbon monoxide (CO) and fine particulate matter (PM2.5) concentrations near a road intersection. Before the predictions, principal component analysis was adopted to generate principal components as input variables to reduce data complexity and collinearity. Then the GAWNN model and the BPNN model were implemented. The comparative results indicated that GAWNN provided more reliable and accurate predictions of CO and PM2.5 concentrations. The results also showed that GAWNN performed better than BPNN did in the capability of forecasting extreme concentrations. Furthermore, the spatial transferability of the GAWNN model was reasonably good despite a degenerated performance caused by the unavoidable difference between the training and test sites. These findings demonstrate the potential of the application of the proposed model to forecast the fine-scale trend of air pollution in the vicinity of a road intersection.

scholarly journals Limiting Factors on Speed and Load of Diesel Engines Vehicle Urban Air Quality Management in Thailand

2015 ◽  
Vol 9 (13) ◽  
pp. 98
Author(s):  
Kritchai Kongkratoke ◽  
Surat Bualert ◽  
Kasem Chunkao
Keyword(s):  
Particulate Matter ◽  
Diesel Engine ◽  
Statistical Significance ◽  
Fine Particulate Matter ◽  
Limiting Factors ◽  
Particulate Matter Emission ◽  
Traffic Jam ◽  
Fine Particulate ◽  
Randomized Design ◽  
The City

Speed and load of diesel engine vehicles are the important factors affected on the fine particulate matter emission in Thailand.<strong> </strong>This study aimed to study the relation of speed and load of diesel engine vehicles affected on the emission of fine particulate matter in Thailand and also related to the emission of the exhaust from diesel engine vehicles. The experiment was designed into a x b Factorial Experiment in Completely Randomized Design. It was divided into 2 experiments as follows: 1) to study the emission of fine particulate matter in Euro 4 diesel engine vehicles, and 2) to study the emission of fine particulate matter in Euro 3 diesel engine vehicles. Moreover, it was to study 3 levels of speed in the driving form of diesel engine vehicles under Bangkok Driving Cycle, 3 levels of load, and 3 replications by using the experiment in Chassis Dynamometer System, and demonstrating the driving similarly to the actual driving. The samples of fine particulate matter from the exhaust were from the speed and load by using Micro-Orifice Uniform Deposition Impactors; MOUDI which had the size from 0.056-18 microns with the flow rate at 30 liter/ minute. The result from this study was found that the experiment of Euro 4 diesel engine vehicles had the factors of speed and load which affected on the emission of fine particulate matter at the level of 0.05, and F-value was 240.03 and 4.60 respectively. About the experiment of Euro 4 diesel engine vehicles, it had the factors of speed and load which affected on the emission of fine particulate matter at the level of 0.05, and F- value was 796.92 and 18.46 respectively. At the speed of 7.8 km/hr, the loads of empty vehicle and 1,000 kg of Euro 3 diesel engine vehicles were different at the level of 0.05. While the speed of 7.8 km/hr was at empty vehicle and 1,000 kg of Euro 4 diesel engine vehicles, it was not different in statistical significance. Therefore, there should be the regulations to control the load of the Euro 4 diesel engine vehicles lower than standard in the city with high traffic jam.

Chinese emissions reductions deliver reduced PM2.5-caused mortality across China during 2015-2017

2020 ◽  
Author(s):  
Ben Silver ◽  
Luke Conibear ◽  
Carly Reddington ◽  
Christophe Knote ◽  
Steve Arnold ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Fine Particulate Matter ◽  
Monitoring Network ◽  
Minor Role ◽  
Chinese Government ◽  
Ozone Concentrations ◽  
National Monitoring ◽  
A Minor

&lt;p&gt;Air pollution is a serious environmental issue and leading contributor to the disease burden in China. Following severe air pollution episodes during the 2012-2013 winter, the Chinese government has prioritised efforts to reduce PM&lt;sub&gt;2.5&lt;/sub&gt; emissions, and established a national monitoring network to record air quality trends. Rapid reductions in fine particulate matter (PM&lt;sub&gt;2.5&lt;/sub&gt;) concentrations and increased ozone concentrations have occurred across China, during 2015 to 2017. We used measurements of particulate matter with a diameter &lt; 2.5 &amp;#181;m (PM&lt;sub&gt;2.5&lt;/sub&gt;) and Ozone (O&lt;sub&gt;3&lt;/sub&gt;) from &gt;1000 stations across China combined with similar datasets from Hong Kong and Taiwan to calculate trends in PM&lt;sub&gt;2.5&lt;/sub&gt;, Nitrogen Dioxide, Sulphur Dioxide and O&lt;sub&gt;3&lt;/sub&gt; across the greater China region during 2015-2019. We then use the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) regional air quality simulations, to explore the drivers and impacts of observed trends. Using annually varying emissions from the Multi-resolution Emission Inventory for China, we simulate air quality across China during 2015-2017, and calculate a median PM&lt;sub&gt;2.5&lt;/sub&gt; trends of -3.9 &amp;#181;g m&lt;sup&gt;-3&lt;/sup&gt; year&lt;sup&gt;-1&lt;/sup&gt;. The measured nationwide median PM&lt;sub&gt;2.5&lt;/sub&gt; trend of -3.4 &amp;#181;g m&lt;sup&gt;-3&lt;/sup&gt; year&lt;sup&gt;-&lt;/sup&gt;. With anthropogenic emissions fixed at 2015-levels, the simulated trend was much weaker (-0.6 &amp;#181;g m&lt;sup&gt;-3&lt;/sup&gt; year&lt;sup&gt;-1&lt;/sup&gt;), demonstrating interannual variability in meteorology played a minor role in the observed PM&lt;sub&gt;2.5&lt;/sub&gt; trend. The model simulated increased ozone concentrations in line with the measurements, but underestimated the magnitude of the observed absolute trend by a factor of 2. We combined simulated trends in PM&lt;sub&gt;2.5&lt;/sub&gt; concentrations with an exposure-response function to estimate that reductions in PM&lt;sub&gt;2.5&lt;/sub&gt; concentrations over this period have reduced PM&lt;sub&gt;2.5&lt;/sub&gt;-attribrutable premature morality across China by 150 000 deaths year&lt;sup&gt;-1&lt;/sup&gt;.&lt;/p&gt;

scholarly journals Air Pollution Analysis during the Lockdown on the City of Milan

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1692
Author(s):  
Nicoletta Lotrecchiano ◽  
Paolo Trucillo ◽  
Diego Barletta ◽  
Massimo Poletto ◽  
Daniele Sofia
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Anthropogenic Activities ◽  
Sudden Change ◽  
Monitoring Network ◽  
Point Of View ◽  
Transport Sector ◽  
Atmospheric Particulates ◽  
Dust Events ◽  
The City

From February 2020, the progressive adoption of measures to contain coronavirus’s contagion has resulted in a sudden change in anthropogenic activities in Italy, especially in Lombardy. From a scientific point of view, this situation represents a unique laboratory for understanding and predicting the consequences of specific measures aimed at improving air quality. In this work, the lockdown effect on Milan’s (Italy) air quality was analyzed. The PM10 and PM2.5 values were measured by the ARPA Lombardia, and the real-time on-road (ROM) air quality monitoring network indicates the seasonality of these pollutants, which typically record the highest values in the coldest months of the year. The 10-year particulate matter concentrations analysis shows a PM10 reduction of 35% from 2010 to 2020. March 2020 data analysis shows an alternation of days with higher and lower particulate matter concentrations; values decrease in pollutants concentrations of 16%, respective to 2018. The complexity of the phenomena related to the atmospheric particulates formation, transport, and accumulation is highlighted by some circumstances, such as the Sahara dust events. The study showed that the trend of a general pollutant concentration reduction should be attributed to the decrease in emissions (specifically, from the transport sector) from the variation of meteorological and environmental conditions.

Spatial Hotspot Analysis of Bucharest’s Urban Heat Island (UHI) Using Modis Data

2018 ◽  
Vol 18 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Georgiana Grigoraș ◽  
Bogdan Urițescu
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Hot Spots ◽  
Land Surface ◽  
Hot Spot ◽  
Monitoring Network ◽  
Residential Areas ◽  
Hotspot Analysis ◽  
The City

Abstract The aim of the study is to find the relationship between the land surface temperature and air temperature and to determine the hot spots in the urban area of Bucharest, the capital of Romania. The analysis was based on images from both moderate-resolution imaging spectroradiometer (MODIS), located on both Terra and Aqua platforms, as well as on data recorded by the four automatic weather stations existing in the endowment of The National Air Quality Monitoring Network, from the summer of 2017. Correlation coefficients between land surface temperature and air temperature were higher at night (0.8-0.87) and slightly lower during the day (0.71-0.77). After the validation of satellite data with in-situ temperature measurements, the hot spots in the metropolitan area of Bucharest were identified using Getis-Ord spatial statistics analysis. It has been achieved that the “very hot” areas are grouped in the center of the city and along the main traffic streets and dense residential areas. During the day the "very hot spots” represent 33.2% of the city's surface, and during the night 31.6%. The area where the mentioned spots persist, falls into the "very hot spot" category both day and night, it represents 27.1% of the city’s surface and it is mainly represented by the city center.

scholarly journals Exploring the Use of PlanetScope Data for Particulate Matter Air Quality Research

2021 ◽  
Vol 13 (15) ◽  
pp. 2981
Author(s):  
Jeanné le Roux ◽  
Sundar Christopher ◽  
Manil Maskey
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Land Cover ◽  
Spectral Response ◽  
Fine Particulate Matter ◽  
Daily Basis ◽  
Land Cover Types ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Difference ◽  
Geolocation Accuracy

Planet, a commercial company, has achieved a key milestone by launching a large fleet of small satellites (smallsats) that provide high spatial resolution imagery of the entire Earth’s surface on a daily basis with its PlanetScope sensors. Given the potential utility of these data, this study explores the use for fine particulate matter (PM2.5) air quality applications. However, before these data can be utilized for air quality applications, key features of the data, including geolocation accuracy, calibration quality, and consistency in spectral signatures, need to be addressed. In this study, selected Dove-Classic PlanetScope data is screened for geolocation consistency. The spectral response of the Dove-Classic PlanetScope data is then compared to Moderate Resolution Imaging Spectroradiometer (MODIS) data over different land cover types, and under varying PM2.5 and mid visible aerosol optical depth (AOD) conditions. The data selected for this study was found to fall within Planet’s reported geolocation accuracy of 10 m (between 3–4 pixels). In a comparison of top of atmosphere (TOA) reflectance over a sample of different land cover types, the difference in reflectance between PlanetScope and MODIS ranged from near-zero (0.0014) to 0.117, with a mean difference in reflectance of 0.046 ± 0.031 across all bands. The reflectance values from PlanetScope were higher than MODIS 78% of the time, although no significant relationship was found between surface PM2.5 or AOD and TOA reflectance for the cases that were studied. The results indicate that commercial satellite data have the potential to address Earth-environmental issues.

scholarly journals Description and Evaluation of the Fine Particulate Matter Forecasts in the NCAR Regional Air Quality Forecasting System

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 302
Author(s):  
Rajesh Kumar ◽  
Piyush Bhardwaj ◽  
Gabriele Pfister ◽  
Carl Drews ◽  
Shawn Honomichl ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Information Dissemination ◽  
Fine Particulate Matter ◽  
Model Errors ◽  
Air Quality Forecast ◽  
Fine Particulate ◽  
Regional Air Quality ◽  
Forecasting System ◽  
Air Quality Forecasting

This paper describes a quasi-operational regional air quality forecasting system for the contiguous United States (CONUS) developed at the National Center for Atmospheric Research (NCAR) to support air quality decision-making, field campaign planning, early identification of model errors and biases, and support the atmospheric science community in their research. This system aims to complement the operational air quality forecasts produced by the National Oceanic and Atmospheric Administration (NOAA), not to replace them. A publicly available information dissemination system has been established that displays various air quality products, including a near-real-time evaluation of the model forecasts. Here, we report the performance of our air quality forecasting system in simulating meteorology and fine particulate matter (PM2.5) for the first year after our system started, i.e., 1 June 2019 to 31 May 2020. Our system shows excellent skill in capturing hourly to daily variations in temperature, surface pressure, relative humidity, water vapor mixing ratios, and wind direction but shows relatively larger errors in wind speed. The model also captures the seasonal cycle of surface PM2.5 very well in different regions and for different types of sites (urban, suburban, and rural) in the CONUS with a mean bias smaller than 1 µg m−3. The skill of the air quality forecasts remains fairly stable between the first and second days of the forecasts. Our air quality forecast products are publicly available at a NCAR webpage. We invite the community to use our forecasting products for their research, as input for urban scale (<4 km), air quality forecasts, or the co-development of customized products, just to name a few applications.

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