Atmosphere

Research

14 pages, 3102 KiB

Open AccessArticle

Lead Isotope Ratio Measurements for Source Identification Using Samples from the UK Heavy Metals Air Quality Monitoring Network

by Emma C. Braysher, Jody H. L. Cheong, David M. Butterfield, Andrew S. Brown and Richard J. C. Brown

Atmosphere 2025, 16(3), 283; https://doi.org/10.3390/atmos16030283 - 27 Feb 2025

Viewed by 431

Abstract

Lead isotope ratios vary depending on the origin of the lead, meaning that characteristic isotopic signatures can be used for source identification in environmental samples. Lead in ambient particulate matter was collected and analysed at 23 monitoring stations as part of the UK [...] Read more.

Lead isotope ratios vary depending on the origin of the lead, meaning that characteristic isotopic signatures can be used for source identification in environmental samples. Lead in ambient particulate matter was collected and analysed at 23 monitoring stations as part of the UK heavy metals air quality monitoring network to assess compliance with legislative limit values for allowable concentrations of lead in air. For the first time on a nationwide UK basis, isotopic analysis of lead was carried out on these samples to gain further information about the origin of the lead and the sources influencing measured concentrations at each of the monitoring stations. These measurements were undertaken with the novel application of ICP–MS/MS for high throughput analysis of over 200 samples from 23 sites across the UK. Values for 207Pb/206Pb ranged from 0.864 to 0.910 with an average standard error of 0.68%, while 208Pb/206Pb values ranged from 2.08 to 2.187 with an average standard error of 0.84%. The dataset was used to draw conclusions as to the main sources of pollution contributing to each site and has demonstrated the utility of ICP–MS/MS as a fit-for-purpose analytical method for the high throughput of a large number of samples in complex matrices. It was possible to identify different source types at the monitoring stations based on the lead isotope signature observed. Comparison with literature values showed clear links with traffic emissions at roadside sites and leaded petrol at a site near an airfield where small aircraft still use this type of fuel. Full article

(This article belongs to the Special Issue Atmospheric Pollutants: Monitoring and Observation)

► Show Figures

Figure 1

17 pages, 7220 KiB

Open AccessArticle

Prolonged Power Outages and Air Quality: Insights from Quito’s 2023–2024 Energy Crisis

by Fidel Vallejo, Patricio Villacrés, Diana Yánez, Lady Espinoza, Elba Bodero-Poveda, Luis Alonso Díaz-Robles, Marcelo Oyaneder, Valeria Campos, Paúl Palmay, Alejandro Cordovilla-Pérez, Valeria Díaz, Jorge Leiva-González and Serguei Alejandro-Martin

Atmosphere 2025, 16(3), 274; https://doi.org/10.3390/atmos16030274 - 26 Feb 2025

Cited by 1 | Viewed by 928

Abstract

The 2023–2024 blackouts in Quito, Ecuador, led to severe air quality deterioration, primarily driven by diesel generator use and increased vehicular traffic. This study analyzed data from seven urban and peri-urban monitoring stations, applying meteorologically normalized data and machine learning models (Boosted Regression [...] Read more.

The 2023–2024 blackouts in Quito, Ecuador, led to severe air quality deterioration, primarily driven by diesel generator use and increased vehicular traffic. This study analyzed data from seven urban and peri-urban monitoring stations, applying meteorologically normalized data and machine learning models (Boosted Regression Trees and Random Forests) to isolate the direct impact of blackouts on pollutant concentrations. The results revealed that PM₁₀ increased by up to 45% and PM_2.5 by 30%, frequently exceeding regulatory limits, particularly in industrial and residential zones. SO₂ exhibited the most extreme rise, surging by 390%, with peak values reaching 500 µg/m³ in areas heavily reliant on high-sulfur diesel generators. The NO₂ concentrations exceeded 200 µg/m³ in high-traffic areas, while O₃ showed dual behavior, decreasing in urban cores due to titration effects but increasing by 15% in suburban valleys, driven by photochemical interactions. A comparison between 2023 and 2024 blackouts highlighted worsening pollution trends, with longer (8–12 h) outages in 2024 causing severe environmental impacts. The findings demonstrate that blackouts significantly worsen air quality, posing critical public health risks. This study underscores the urgent need for policy interventions to mitigate the environmental impact of energy disruptions. Key recommendations include stricter fuel quality standards, diesel generator emission controls, and an accelerated transition to renewable energy. These results provide scientific evidence for future environmental regulations, supporting sustainable air quality management strategies to minimize future energy crises’ health and ecological consequences. Full article

(This article belongs to the Special Issue Atmospheric Pollutants: Monitoring and Observation)

► Show Figures

Figure 1

25 pages, 8643 KiB

Open AccessArticle

Investigating Meteorological Factors Influencing Pollutant Concentrations and Copernicus Atmosphere Monitoring Service (CAMS) Model Forecasts in the Tehran Metropolis

by Sara Karami, Zahra Ghassabi, Noushin Khoddam and Maral Habibi

Atmosphere 2025, 16(3), 264; https://doi.org/10.3390/atmos16030264 - 24 Feb 2025

Cited by 1 | Viewed by 472

Abstract

In recent years, air pollution has become a significant issue for megacities. This study analyzed the air pollution levels in Tehran and the relationship between pollutant concentrations and atmospheric quantities during 2023. The correlation coefficients between wind speed, temperature, mean sea level pressure [...] Read more.

In recent years, air pollution has become a significant issue for megacities. This study analyzed the air pollution levels in Tehran and the relationship between pollutant concentrations and atmospheric quantities during 2023. The correlation coefficients between wind speed, temperature, mean sea level pressure (MSLP), and relative humidity (RH) were calculated against the concentrations of NO₂, NO_x, PM₁₀, and PM_2.5. Additionally, one case study was conducted for each pollutant. Approximately 72% of haze phenomena in Tehran were recorded in November, December, and January. The monthly pattern of PM₁₀ concentration indicated higher levels in the southern and western parts of Tehran. For PM_2.5, in addition to these areas, significant concentrations were also observed in the central and eastern parts. NO₂ concentrations were found to be higher in the northeast and northern areas. An inverse relationship was found between wind speed and temperature with pollutant concentrations. Positive correlations between MSLP and pollutant concentrations suggested that the pollutant levels also increased as air pressure rose. RH showed a significant direct relationship with PM_2.5 and NO_x. Synoptic analysis revealed that PM₁₀ case studies often occurred during the warm season, with a thermal low pressure situated over the Iranian plateau. During PM_2.5 and NO₂ pollution events, Tehran was influenced by high pressure, and 10 m wind speeds were weak. Finally, verification of the 24 h forecast of the CAMS model showed that, while the model accurately predicted the spatial distribution of pollutants in most cases, it consistently underestimated the concentration levels. Full article

(This article belongs to the Special Issue Atmospheric Pollutants: Monitoring and Observation)

► Show Figures

Figure 1

19 pages, 3558 KiB

Open AccessArticle

Spatial–Temporal Variation and the Influencing Factors of NO₂ Column Concentration in the Plateau Mountains of Southwest China

by Fei Dong, Zhongfa Zhou, Denghong Huang, Xiandan Du and Shuanglong Du

Atmosphere 2024, 15(11), 1263; https://doi.org/10.3390/atmos15111263 - 22 Oct 2024

Viewed by 958

Abstract

Given the complex terrain and economic development status of Guizhou Province, research on tropospheric NO₂ column concentration using satellite remote sensing is still insufficient. Observing the spatial–temporal evolution characteristics of tropospheric NO₂ column concentration can ensure the stable development of air [...] Read more.

Given the complex terrain and economic development status of Guizhou Province, research on tropospheric NO₂ column concentration using satellite remote sensing is still insufficient. Observing the spatial–temporal evolution characteristics of tropospheric NO₂ column concentration can ensure the stable development of air quality. Based on the Google Earth Engine (GEE) platform, NO₂ column concentration data retrieved from Sentinel-5P TROPOMI were analyzed using spatial autocorrelation, hotspot analysis, and geographic detector methods (Geodetector). The results show that NO₂ column concentration in Guizhou Province exhibits seasonal variation, characterized by higher levels in winter and lower levels in summer, with transitional values in spring and autumn. The annual average concentration was highest in 2021 at 3.47 × 10⁻⁵ mol/m² and lowest in 2022 at 2.85 × 10⁻⁵ mol/m². Spatially, NO₂ column concentration displays a distribution pattern of “high in the west, low in the east; high in the north, low in the south”, with significant spatial clustering. The distribution of cold and hot spots aligns with areas of high and low values. NO₂ column concentration is primarily influenced by socio-economic factors, with the interaction between any two factors enhancing the explanatory power of individual factors on NO₂ column concentration. Full article

(This article belongs to the Special Issue Atmospheric Pollutants: Monitoring and Observation)

► Show Figures

Figure 1

13 pages, 3866 KiB

Open AccessArticle

The Development and Optimization of a New Wind Tunnel Design for Odour Sampling

by Francesca Tagliaferri, Luca Carrera, Anna Albertini, Marzio Invernizzi and Selena Sironi

Atmosphere 2024, 15(10), 1181; https://doi.org/10.3390/atmos15101181 - 30 Sep 2024

Cited by 1 | Viewed by 1141

Abstract

The characterization of passive area sources, emitting odours due to wind-driven convection, poses significant challenges. The present experimental study aims to evaluate the performance, in terms of fluid dynamics and mass transfer, of a recently developed wind tunnel, with a more compact design [...] Read more.

The characterization of passive area sources, emitting odours due to wind-driven convection, poses significant challenges. The present experimental study aims to evaluate the performance, in terms of fluid dynamics and mass transfer, of a recently developed wind tunnel, with a more compact design and reduced weight, compared to the one proposed by the Italian regulations. The results show that the new design outperforms the Italian standard in several aspects. From a fluid dynamic point of view, the new wind tunnel exhibits a slightly more homogenous and uniform velocity distribution, and it does not reveal airflow preferential channels inside the central body. The pressure tests highlight that the presence of fillers in the new wind tunnel does not significantly alter the pressure inside the hood and therefore the gas–liquid equilibrium conditions; actually, the slight overpressure may help to prevent the infiltration of external air. Finally, mass transfer tests on the standard device show a vertical concentration gradient along the outlet duct, highlighting concentration values that differ up to a factor of two depending on the measurement point. The new design has almost completely solved this issue, thanks to the use of fillers that promote mixing of the outlet flow. Full article

(This article belongs to the Special Issue Atmospheric Pollutants: Monitoring and Observation)

► Show Figures

Figure 1

21 pages, 5032 KiB

Open AccessArticle

Evaluation of Fine Particulate Matter (PM_2.5) Concentrations Measured by Collocated Federal Reference Method and Federal Equivalent Method Monitors in the U.S.

by Tanvir R. Khan, Zachery I. Emerson and Karen H. Mentz

Atmosphere 2024, 15(8), 978; https://doi.org/10.3390/atmos15080978 - 15 Aug 2024

Cited by 2 | Viewed by 1785

Abstract

The comparison between Federal Equivalent Method (FEM) and Federal Reference Method (FRM) monitors in measuring fine particulate matter (PM_2.5) concentrations frequently raises concerns about the accuracy and reliability of data. The comparability, or lack thereof, of data between FRM and FEM [...] Read more.

The comparison between Federal Equivalent Method (FEM) and Federal Reference Method (FRM) monitors in measuring fine particulate matter (PM_2.5) concentrations frequently raises concerns about the accuracy and reliability of data. The comparability, or lack thereof, of data between FRM and FEM monitors may have significant implications for maintaining compliance with the National Ambient Air Quality Standards (NAAQSs). This study investigates the performance of continuous FEM monitors collocated with FRM monitors across 10 EPA regions in the U.S., focusing on PM_2.5 measurements collected from 276 monitoring stations. Through an analysis of annually averaged paired concentration data, the study examines concentration ratios (FEM/FRM) and associated biases (in %, defined as [(FEM/FRM)−1] × 100) in FEM monitors across different manufacturers, measurement methods, EPA regions, and sampling location types. The study findings reveal a varied distribution of FEM/FRM ratios, with more than 50% of the FEM monitors having FEM/FRM > 1.1 and approximately 30% having FEM/FRM > 1.2. Substantial variations in estimated biases are identified among monitor types, measurement methods, EPA regions, and sampling site locations. Light scatter-based FEM monitors, notably Teledyne models 640 and 640x, dominate all locations (urban, suburban, and rural), with rural areas exhibiting higher mean bias values for both light scatter and beta attenuation FEM monitors (41% and 23%, respectively). On average, light scatter-based FEM monitors demonstrate higher biases compared to beta attenuation monitors across all EPA regions (28% vs. 12%). Irrespective of the measurement method employed, FEM monitors demonstrate a significant positive bias (mean bias 22%) relative to FRM monitors, which could result in an overestimation of PM_2.5 design values (DVs) by 13–21% at monitoring sites designating FEMs as primary monitors for NAAQSs compliance designations. These findings emphasize the critical need to address method comparability issues, especially considering the recent tightening of NAAQSs for PM_2.5 (annual) from 12 µg/m³ to 9 µg/m³ in the U.S. Full article

(This article belongs to the Special Issue Atmospheric Pollutants: Monitoring and Observation)

► Show Figures

Figure 1

14 pages, 2447 KiB

Open AccessArticle

Air Quality Prediction and Ranking Assessment Based on Bootstrap-XGBoost Algorithm and Ordinal Classification Models

by Jingnan Yang, Yuzhu Tian and Chun Ho Wu

Atmosphere 2024, 15(8), 925; https://doi.org/10.3390/atmos15080925 - 2 Aug 2024

Cited by 3 | Viewed by 1598

Abstract

Along with the rapid development of industries and the acceleration of urbanisation, the problem of air pollution is becoming more serious. Exploring the relevant factors affecting air quality and accurately predicting the air quality index are significant in improving the overall environmental quality [...] Read more.

Along with the rapid development of industries and the acceleration of urbanisation, the problem of air pollution is becoming more serious. Exploring the relevant factors affecting air quality and accurately predicting the air quality index are significant in improving the overall environmental quality and realising green economic development. Machine learning algorithms and statistical models have been widely used in air quality prediction and ranking assessment. In this paper, based on daily air quality data for the city of Xi’an, China, from 1 October 2022 to 30 September 2023, we construct support vector regression (SVR), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), random forests (RF), neural network (NN) and long short-term memory (LSTM) models to analyse the influence of the air quality index for Xi’an and to conduct comparative tests. The predicted values and 95% prediction intervals of the AQI for the next 15 days for Xi’an, China, are given based on the Bootstrap-XGBoost algorithm. Further, the ordinal logit regression and ordinal probit regression models are constructed to evaluate and accurately predict the AQI ranks of the data from 1 October 2023 to 15 October 2023 for Xi’an. Finally, this paper proposes some suggestions and policy measures based on the findings of this paper. Full article

(This article belongs to the Special Issue Atmospheric Pollutants: Monitoring and Observation)

► Show Figures

Figure 1

20 pages, 2846 KiB

Open AccessArticle

Combining the Emission Preprocessor HERMES with the Chemical Transport Model TM5-MP

by Sarah-Lena Seemann, Nikos Daskalakis, Kun Qu and Mihalis Vrekoussis

Atmosphere 2024, 15(4), 469; https://doi.org/10.3390/atmos15040469 - 10 Apr 2024

Viewed by 1665

Abstract

Emission inventories (EIs) are vital for air quality modeling. Specific research goals often require modifying EIs from diverse data sources, demanding significant code development. In this study, we utilized and further developed the High Elective Resolution Modeling Emission System version three for Global [...] Read more.

Emission inventories (EIs) are vital for air quality modeling. Specific research goals often require modifying EIs from diverse data sources, demanding significant code development. In this study, we utilized and further developed the High Elective Resolution Modeling Emission System version three for Global and Regional domains (HERMESv3_gr). This user-friendly processing system was adapted for generating EIs compatible with the Chemistry Transport Model Tracel Model 5 Massive Parallel (TM5-MP). The results indicate that HERMESv3_gr is capable of generating EIs with negligible biases (

10^{- 7}

relative differences) for TM5-MP, showcasing its effectiveness. We applied HERMESv3_gr to integrate the EI Regional Emission inventory in Asia (REAS) into the global EI Community Emission Data System (CEDS). Comparison of model results using CEDS alone and the integrated EI against measurement data for various pollutants globally revealed small improvements for carbon monoxide (1%) ethane (1–2%), and nitrogen oxide (2%) and larger for propane (5–7%). Ozone in the northern hemisphere improved by about 2% while in the southern hemisphere improvements of 5% could be observed. Our findings highlight the importance of carefully considering the effects of EI integration for accurate air quality modeling. Full article

(This article belongs to the Special Issue Atmospheric Pollutants: Monitoring and Observation)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Atmospheric Pollutants: Monitoring and Observation

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (8 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI