FRI0531 AIR POLLUTANTS AND DEVELOPMENT OF INTERSTITIAL LUNG DISEASE IN PATIENTS WITH AUTOIMMUNE DISEASES

Background:Interstitial lung disease (ILD) is characterized by progressive inflammation and fibrosis, and accumulating evidence have shown that exposure to air pollutants was associated with the development of ILD. Autoimmune diseases are highly correlated with ILD, including connective tissue disease-associated ILD (CTD-ILD) as well as interstitial pneumonia with autoimmune features (IPAF), and the development of ILD is a crucial cause of morbidity and mortality in patients with autoimmune diseases. One recent Taiwanese study reported that exposure to air pollutants was associated with incident systemic lupus erythematosus (SLE). However, the impact of air pollutants on the development of ILD among patients with autoimmune diseases remains unknown.Objectives:The study aimed to address the impact of accumulating exposure to air pollutant above moderate level, defined by Air Quality Index (AQI) value higher than 50, on the development of ILD in patients with autoimmune diseases including SLE, rheumatoid arthritis (RA) and primary Sjögren’s syndrome (SS).Methods:We used a National Health Insurance Research Database in Taiwan to enroll patients with SLE (International Classification of Diseases (ICD)-9 code 710.0, n=13,211), RA (ICD-9 code 714.0 and 714.30–714.33, n=32,373), and primary SS (ICD-9 code, 710.0, n=15,246) between 2001 and 2013. We identified newly diagnosed ILD cases (ICD-code 515) between 2012 and 2013 and selected age, sex, disease duration and index-year matched (1:4) patients as non-ILD controls. The hourly levels of air pollutants one year prior to the index-date were obtained from 60 air quality monitoring stations across Taiwan, and the air pollutants in the present study consisted of particulate matter <2.5 μm in size (PM2.5), particulate matter <10 μm in size (PM10), nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2) and ozone (O3). We used a spatio-temporal model built by a deep-learning mechanism to estimate levels of air pollutants at 374 residential locations based on data of 3 air quality monitoring stations near the location (8). Notably, we used cumulative exposed hours to air pollutants higher than modest level, defined by AQI criteria, given that daily mean level of air pollutants might possibly underestimate the triggered inflammatory effect by a temporary exposure of high-level air pollutant. A conditional logistic regression was used to determine the association between exposure to air pollutant and the development of ILD, adjusting age, gender, Charlson Comorbidity Index (CCI), urbanization, family income, and medications for autoimmune diseases.Results:A total of 272 patients with newly diagnosed ILD were identified among patients with autoimmune diseases, including 39 with SLE, 135 with RA, and 98 with primary SS. We found that the duration of exposure to PM 2.5 higher than modest level was associated with the risk of ILD development in patients with SS (adjOR 1.07, 95% CI 1.01–1.13), and similar trends were also found in patients with SLE (adjOR 1.03, 95% CI 0.95–1.12) and RA (adjOR 1.03, 95% CI 0.99–1.07). Intriguingly, we observed an inverse correlation between the duration of exposure to O3 and the development of ILD in patients with SS (adjOR 0.83, 95% CI 0.70–0.99); however, the finding was not found in patients with SLE (adjOR 1.13, 95% CI 0.92–1.37) and RA (adjOR 0.98, 95% CI 0.87–1.11).Conclusion:In conclusion, we identified that longer exposure to PM2.5 higher than modest level tended to be associated with the development of ILD in patients with autoimmune diseases, mainly SS.References:[1] Araki T, Putman RK, Hatabu H, Gao W, Dupuis J, Latourelle JC, et al. Development and Progression of Interstitial Lung Abnormalities in the Framingham Heart Study. Am J Respir Crit Care Med 2016;194:1514-1522.[2] Tang KT, Tsuang BJ, Ku KC, Chen YH, Lin CH, Chen DY. Relationship between exposure to air pollutants and development of systemic autoimmune rheumatic diseases: a nationwide population-based case-control study. Ann Rheum Dis 2019;78:1288-1291.Disclosure of Interests:Hsin-Hua Chen: None declared, Wen-Cheng Chao: None declared, Yi-Hsing Chen Grant/research support from: Taiwan Ministry of Science and Technology, Taiwan Department of Health, Taichung Veterans General Hospital, National Yang-Ming University, GSK, Pfizer, BMS., Consultant of: Pfizer, Novartis, Abbvie, Johnson & Johnson, BMS, Roche, Lilly, GSK, Astra& Zeneca, Sanofi, MSD, Guigai, Astellas, Inova Diagnostics, UCB, Agnitio Science Technology, United Biopharma, Thermo Fisher, Gilead., Paid instructor for: Pfizer, Novartis, Johnson & Johnson, Roche, Lilly, Astra& Zeneca, Sanofi, Astellas, Agnitio Science Technology, United Biopharma., Speakers bureau: Pfizer, Novartis, Abbvie, Johnson & Johnson, BMS, Roche, Lilly, GSK, Astra& Zeneca, Sanofi, MSD, Guigai, Astellas, Inova Diagnostics, UCB, Agnitio Science Technology, United Biopharma, Thermo Fisher, Gilead., Der-Yuan Chen: None declared, Ching-Heng Lin: None declared

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Air Pollutants Prediction in Shenzhen Based on ARIMA and Prophet Method

E3S Web of Conferences ◽

10.1051/e3sconf/201913605001 ◽

2019 ◽

Vol 136 ◽

pp. 05001 ◽

Cited By ~ 2

Author(s):

Ziyuan Ye

Keyword(s):

Air Quality ◽

Hybrid Model ◽

Air Pollutants ◽

Moving Average ◽

Mixing Time ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Autoregressive Integrated Moving Average ◽

Moving Average Model ◽

Air Quality Monitoring Stations

In order to improve the accuracy of predicting the air pollutants in Shenzhen, a hybrid model based on ARIMA (Autoregressive Integrated Moving Average model) and prophet for mixing time and space relationships was proposed. First, ARIMA and Prophet method were applied to train the data from 11 air quality monitoring stations and gave them different weights. Then, finished the calculation about weight of impact in each air quality monitoring station to final results. Finally, built up the hybrid model and did the error evaluation. The result of the experiments illustrated that this hybrid method can improve the air pollutants prediction in Shenzhen.

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VARMA-EGARCH Model for Air-Quality Analyses and Application in Southern Taiwan

Atmosphere ◽

10.3390/atmos11101096 ◽

2020 ◽

Vol 11 (10) ◽

pp. 1096

Author(s):

Edward Ming-Yang Wu ◽

Shu-Lung Kuo

Keyword(s):

Time Series ◽

Air Quality ◽

Air Pollutants ◽

Quality Monitoring ◽

Quality Data ◽

Air Quality Monitoring ◽

Egarch Model ◽

Southern Taiwan ◽

Air Quality Monitoring Stations ◽

Monitoring Stations

This study adopted the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model to analyze seven air pollutants (or the seven variables in this study) from ten air quality monitoring stations in the Kaohsiung–Pingtung Air Pollutant Control Area located in southern Taiwan. Before the verification analysis of the EGARCH model is conducted, the air quality data collected at the ten air quality monitoring stations in the Kaohsiung–Pingtung area are classified into three major factors using the factor analyses in multiple statistical analyses. The factors with the most significance are then selected as the targets for conducting investigations; they are termed “photochemical pollution factors”, or factors related to pollution caused by air pollutants, including particulate matter with particles below 10 microns (PM10), ozone (O3) and nitrogen dioxide (NO2). Then, we applied the Vector Autoregressive Moving Average-EGARCH (VARMA-EGARCH) model under the condition where the standardized residual existed in order to study the relationships among three air pollutants and how their concentration changed in the time series. By simulating the optimal model, namely VARMA (1,1)-EGARCH (1,1), we found that when O3 was the dependent variable, the concentration of O3 was not affected by the concentration of PM10 and NO2 in the same term. In terms of the impact response analysis on the predictive power of the three air pollutants in the time series, we found that the asymmetry effect of NO2 was the most significant, meaning that NO2 influenced the GARCH effect the least when the change of seasons caused the NO2 concentration to fluctuate; it also suggested that the concentration of NO2 produced in this area and the degree of change are lower than those of the other two air pollutants. This research is the first of its kind in the world to adopt a VARMA-EGARCH model to explore the interplay among various air pollutants and reactions triggered by it over time. The results of this study can be referenced by authorities for planning air quality total quantity control, applying and examining various air quality models, simulating the allowable increase in air quality limits, and evaluating the benefit of air quality improvement.

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A numerical study of the contribution to the air pollutant in Beijing during CAREBeijing-2006

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-11-5271-2011 ◽

2011 ◽

Vol 11 (2) ◽

pp. 5271-5312 ◽

Cited By ~ 2

Author(s):

Q. Z. Wu ◽

Z. F. Wang ◽

A. Gbaguidi ◽

C. Gao ◽

L. N. Li ◽

...

Keyword(s):

Surface Layer ◽

Air Quality ◽

Rural Areas ◽

Air Pollutants ◽

High Performance ◽

Numerical Study ◽

Air Pollutant ◽

Air Quality Prediction ◽

Regional Sources ◽

The Impact

Abstract. An online air pollutant tagged module has been developed in the Nested Air Quality Prediction Model System (NAQPMS) to investigate the impact of local and regional sources on the air pollutants in Beijing during the Campaign of Air Quality Research in Beijing 2006 (CAREBeijing-2006). The NAQPMS model shows high performance in simulating sulfur dioxide (SO2), particulate matter (PM10), nitrogen dioxide (NO2), and ozone (O3) with overall better agreements with the observations at urban sites than rural areas. With the tagged module, the air pollutant contributions from local and regional sources to the surface layer (about 30 m) and the upper layer (about 1.1 km) in Beijing are differentiated and estimated. The air pollutants at the surface layer in Beijing are dominated by the contributions from local sources, accounting for 65% of SO2, 75% of PM10 and nearly 90% of NO2, respectively, comparatively, the upper layer has large source contributions from the surrounding regions (e.g., southern Beijing), accounting for more than 50% of the SO2 and PM10 concentrations. Country scale analysis is also performed and the results suggest that Tianjin is the dominant source of SO2 in Pinggu County, and Langfang, Hebei is the most important regional contributor to PM10 in Beijing. Moreover, the surrounding regions show larger impact on SO2, PM10 and NO2 in the eastern counties of Beijing (e.g., Pinggu, Tongzhou and Daxing) than those in western Beijing, which is likely due to the Beijing's semi-basin topography and the summer monsoon. Our results indicate that the efforts to control the air pollutants in Beijing should focus on controlling both local and regional emissions.

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NGHIÊN CỨU ỨNG DỤNG MẠNG TRẠNG THÁI PHẢN HỒI ĐỂ DỰ BÁO CHẤT LƯỢNG KHÔNG KHÍ

Vietnam Journal of Science and Technology ◽

10.15625/0866-708x/54/1/5989 ◽

2016 ◽

Vol 54 (1) ◽

pp. 54 ◽

Cited By ~ 1

Author(s):

Mac Duy Hung ◽

Nghiem Trung Dung

Keyword(s):

Air Quality ◽

Air Pollutants ◽

Meteorological Parameters ◽

Air Pollutant ◽

Training Data ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Multilayer Perception ◽

Testing Data ◽

Better Than

A study on the application of Echo State Network (ESN) for the forecast of air quality in Hanoi for a period of seven days, which is based on the nonlinear relationships between the concentrations of an air pollutant to be forecasted and meteorological parameters, was conducted. Three air pollutants being SO2, NO2 and PM10 were selected for this study. Training data and testing data were extracted from the database of Lang air quality monitoring station, Hanoi, from 2003 to 2009. Values forecasted by ESN are compared with those by MLP (Multilayer Perception). Results shown that, in almost experiments, the performance of ESN is better than that of MLP in terms of the values and the correlation of concentration trends. The averages of RMSE of ESN and MLP for SO2 are 5.9 ppb and 6.9 ppb, respectively. For PM10, the accuracy of ESN is 83.8% with MAE of 53.5 μg/m3, while the accuracy of MLP is only 77.6% with MAE of 68.2 μg/m3. For NO2, the performance of ESN and MLP is similar; the accuracy of both models is in the range of 60% to 72.7%. These suggest that, ESN is a novel and feasible approach to build the air forecasting model. Keywords: Forecast, air quality, ESN, MLP, ANN, Hanoi, Vietnam.

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Short-term environmental impact of ambient air quality trends in during the COVID-19 pandemic in India

Annals of Civil and Environmental Engineering ◽

10.29328/journal.acee.1001031 ◽

2021 ◽

Vol 5 (1) ◽

pp. 017-025

Author(s):

Karuppasamy Manikanda Bharath ◽

Natesan Usha ◽

Periyasamy Balamadeswaran ◽

S Srinivasalu

Keyword(s):

Air Quality ◽

Ambient Air ◽

Air Pollutant ◽

Quality Monitoring ◽

Quality Data ◽

Air Quality Monitoring ◽

The North ◽

Air Quality Monitoring Stations ◽

Air Quality Data ◽

Monitoring Stations

The lockdown, implemented in response to the COVID-19 epidemic, restricted the operation of various sectors in the country and its highlights a good environmental outcome. Thus, a comparison of air pollutants in India before and after the imposed lockdown indicated an overall improvement air quality across major Indian cities. This was established by utilizing the Central Pollution Control Board’s database of air quality monitoring station statistics, such as air quality patterns. During the COVID-19 epidemic, India’s pre-to-post nationwide lockdown was examined. The air quality data was collected from 30-12-2019 to 28-04-2020 and synthesized using 231 Automatic air quality monitoring stations in a major Indian metropolis. Specifically, air pollutant concentrations, temperature, and relative humidity variation during COVID-19 pandemic pre-to-post lockdown variation in India were monitored. As an outcome, several cities around the country have reported improved air quality. Generally, the air quality, on a categorical scale was found to be ‘Good’. However, a few cities from the North-eastern part of India were categorized as ‘Moderate/Satisfactory’. Overall, the particulate matters reduction was in around 60% and other gaseous pollutants was in 40% reduction was observed during the lockdown period. The results of this study include an analysis of air quality data derived from continuous air quality monitoring stations from the pre-lockdown to post-lockdown period. Air quality in India improved following the national lockdown, the interpretation of trends for PM 2.5, PM 10, SO2, NO2, and the Air Quality Index has been provided in studies for major cities across India, including Delhi, Gurugram, Noida, Mumbai, Kolkata, Bengaluru, Patna, and others.

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Intra-community air quality monitoring in various urban microenvironments in South Korea: based on observations from highly dense cost-effective sensor network

10.5194/egusphere-egu2020-19324 ◽

2020 ◽

Author(s):

Yongmi Park ◽

Ho-Sun Park ◽

Wonsik Choi

Keyword(s):

Air Quality ◽

Sensor Network ◽

Air Pollutants ◽

Sensor Node ◽

Cost Effective ◽

Air Pollutant ◽

Sensor Nodes ◽

Quality Monitoring ◽

Built Environments ◽

Air Quality Monitoring

&#160;As urbanization has spread, increased energy consumption, complicated built environments, and dense road networks cause spatiotemporal heterogeneity of air pollutant distributions even in an intra-community scale. High spatiotemporal heterogeneity of air pollutant distributions can affect pedestrian and/or traffic users&#8217; exposure to air pollutants according to where and when they are, potentially forming air pollution hotspots. Thus, it is important to understand the characteristics of spatiotemporal distributions in air pollutants in various micro-built environments in populated urban areas. However, current air quality monitoring performed by the government cannot capture these highly heterogeneous distributions of air pollutants due to the limitations of financial and human resources. In this respect, cost-effective sensors have great potential to build highly spatially dense air quality monitoring networks to address the low spatial resolution issue of conventional air quality monitoring stations.In this study, we built a highly dense air quality monitoring network consisting of 30 sets of sensor nodes in an 800 m &#180; 800 m spatial domain to understand the characteristics of air pollutant distributions in various urban microenvironments. The domain includes urban street canyon with moderate traffic, a mixture of high and low buildings with high traffic, an open space with minimal traffic, and others. The sensor node consists of sensors (for CO, NO2, O3, PM2.5, and PM10, temperature, and humidity) and communication/data storage parts (wifi, interface for smartphone connection, and SD card). We also conducted inter-sensor comparison among sensor nodes and intercomparison tests between the sensor node and conventional reference instruments.Intra-community air quality monitoring with a sensor network was conducted for a couple of weeks in two distinct weather conditions (humid and hot summer and dry and cold winter) in 2017 and 2018. During the observation periods, the concentration distribution analyses for air pollutants (except CO, PM) showed significant heterogeneity in their distributions in space. In addition, the correlation analysis with the meteorological factors showed that CO concentrations were affected by wind speed (winter, R2=0.22-0.25), but the other air pollutants were not directly correlated. We also examined the effects of land-use and building configuration on air pollution distributions. More details concerning these results are presented.Keywords: Sensor network, low-cost sensor, spatial heterogeneity, micro-built environments

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A numerical study of contributions to air pollution in Beijing during CAREBeijing-2006

Atmospheric Chemistry and Physics ◽

10.5194/acp-11-5997-2011 ◽

2011 ◽

Vol 11 (12) ◽

pp. 5997-6011 ◽

Cited By ~ 66

Author(s):

Q. Z. Wu ◽

Z. F. Wang ◽

A. Gbaguidi ◽

C. Gao ◽

L. N. Li ◽

...

Keyword(s):

Surface Layer ◽

Air Quality ◽

Rural Areas ◽

Air Pollutants ◽

High Performance ◽

Numerical Study ◽

Air Pollutant ◽

Air Quality Prediction ◽

Regional Sources ◽

The Impact

Abstract. An online air pollutant tagged module has been developed in the Nested Air Quality Prediction Model System (NAQPMS) to investigate the impact of local and regional sources on the air pollutants in Beijing during the Campaign of Air Quality Research in Beijing 2006 (CAREBeijing-2006). The NAQPMS model shows high performance in simulating sulfur dioxide (SO2), particulate matter (PM10), nitrogen dioxide (NO2), and ozone (O3) with overall better agreements with the observations at urban sites than rural areas. With the tagged module, the air pollutant contributions from local and regional sources to the surface layer (about 30 m) and the upper layer (about 1.1 km) in Beijing are differentiated and estimated. The air pollutants at the surface layer in Beijing are dominated by the contributions from local sources, accounting for 65 % of SO2, 75 % of PM10 and nearly 90 % of NO2, respectively, comparatively, the 1.1 km layer has large source contributions from the surrounding regions (e.g., southern Beijing), accounting for more than 50 % of the SO2 and PM10 concentrations. County scale analysis is also performed and the results suggest that Tianjin is the dominant source of SO2 in Pinggu County, and Langfang, Hebei is the most important regional contributor to PM10 in Beijing. Moreover, the surrounding regions show larger impact on SO2, PM10 and NO2 in the eastern counties of Beijing (e.g., Pinggu, Tongzhou and Daxing) than those in western Beijing, which is likely due to the Beijing's semi-basin topography and the summer monsoon. Our results indicate that the efforts to control the air pollutants in Beijing should focus on controlling both local and regional emissions.

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IMPACT OF URBAN LAND USES AND ACTIVITES ON THE AMBIENT AIR QUALITY IN KLANG VALLEY, MALAYSIA FROM 2014 TO 2020

PLANNING MALAYSIA JOURNAL ◽

10.21837/pm.v18i14.829 ◽

2020 ◽

Vol 18 (14) ◽

Author(s):

Oliver Hoon Leh Ling ◽

Marlyana Azyyati Marzukhi ◽

Jie Kwong Qi ◽

Nurul Ashikin Mabahwi

Keyword(s):

Air Quality ◽

Urban Areas ◽

Air Pollutants ◽

Ambient Air ◽

Air Pollutant ◽

Land Uses ◽

Ambient Air Quality ◽

Residential Areas ◽

Klang Valley ◽

The Impact

Ambient air in the urban area normally is more polluted than less developed areas. This is due to the concentration of urban activities, such as industrial, transportations and commercial or business activities. A study about the impact of urban land uses and activities on the levels of air pollutants in Malaysia’s most urbanised and most developed region that is Klang Valley was conducted. Data of Air Pollutant Index (API) and average concentration of selected air pollutants were used to analyse the ambient air quality of the selected five (5) cities or towns in Klang Valley. The air quality condition of the five (5) cities or towns were related to the land use distributions of the cities or towns with a purpose to understand the impact of land uses on the ambient air quality. Furthermore, the changes of ambient air quality before and after Movement Control Order (MCO) were analysed to examine the impact of human activity changes on the ambient air quality. The study found that a city or a town with more industrial and transportation land uses with fewer greens was more polluted than the area with less industrial and transportation land uses with more greens. However, this finding did not apply to all areas due to effect of winds on the distribution of air pollutants. Besides that, because of MCO, most people stayed at home with the mode of “work from home” that caused air pollutant levels in urban areas to decrease due to less urban activities. Nevertheless, there was a risk of an increase in air pollution levels in residential areas due to the concentration of activities, especially driving motor vehicles in residential areas. A recommendation is given to encourage “work from home” and reduce dependency on auto-mobile in residential areas in order to improve the air quality in urban areas.

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Emission Scenario Assessment of Gasohol Reformulation Proposals and Ethanol Use in the Metropolitan Area of São Paulo

The Open Atmospheric Science Journal ◽

10.2174/1874282300802010166 ◽

2008 ◽

Vol 2 (1) ◽

pp. 166-175 ◽

Cited By ~ 11

Author(s):

L.D. Martins ◽

M.F. Andrade

Keyword(s):

Air Quality ◽

Metropolitan Area ◽

Sao Paulo ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Emission Scenarios ◽

Ozone Concentrations ◽

Air Quality Monitoring Stations ◽

The Impact ◽

Monitoring Stations

The frequent episodes of high concentrations of ozone and of inhalable particulate matter occurring in the Metropolitan Area of Sao Paulo (MASP) are primarily associated with vehicle emissions. The objective of this study was to evaluate the impact of the use of reformulations of the gasoline-ethanol blend known as gasohol and of ethanol on the ozone formation. A three-dimensional photochemical model was employed to estimate the sensitivity of ozone and evaluate the implementation of emission scenarios, considering various fuel formulations, in the MASP. The base case ozone concentrations were consistent with the observations over six air quality monitoring stations located in the MASP, suggesting that the model can be used to evaluate the impact that various emission scenarios would have on ozone levels. Six scenarios were analyzed; scenarios 1 to 5 involved reductions in compounds found in gasohol in various proportions compared with the base emission inventory and scenario 6 specified that the entire light duty fleet would burn pure ethanol. In scenario 3 (reductions in olefins, aromatics and benzene) and scenario 5 (reductions in the five species that are associated with higher ozone sensitivity), ozone concentrations were below the national standard only at the air quality monitoring stations (not domain-wide). Our results suggest that implementing scenario 6 would improve air quality in the MASP.

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Evolution of Gaseous and Particulate Pollutants in the Air: What Changed after Five Lockdown Weeks at a Southwest Atlantic European Region (Northwest of Spain) Due to the SARS-CoV-2 Pandemic?

Atmosphere ◽

10.3390/atmos12050562 ◽

2021 ◽

Vol 12 (5) ◽

pp. 562

Author(s):

Jorge Moreda-Piñeiro ◽

Joel Sánchez-Piñero ◽

María Fernández-Amado ◽

Paula Costa-Tomé ◽

Nuria Gallego-Fernández ◽

...

Keyword(s):

Air Quality ◽

Road Traffic ◽

Statistical Significance ◽

Temporal Trends ◽

Air Pollutant ◽

Concentration Data ◽

Southwest Atlantic ◽

Pollutant Concentrations ◽

Air Quality Monitoring Stations ◽

Almost All

Due to the exponential growth of the SARS-CoV-2 pandemic in Spain (2020), the Spanish Government adopted lockdown measures as mitigating strategies to reduce the spread of the pandemic from 14 March. In this paper, we report the results of the change in air quality at two Atlantic Coastal European cities (Northwest Spain) during five lockdown weeks. The temporal evolution of gaseous (nitrogen oxides, comprising NOx, NO, and NO2; sulfur dioxide, SO2; carbon monoxide, CO; and ozone, O3) and particulate matter (PM10; PM2.5; and equivalent black carbon, eBC) pollutants were recorded before (7 February to 13 March 2020) and during the first five lockdown weeks (14 March to 20 April 2020) at seven air quality monitoring stations (urban background, traffic, and industrial) in the cities of A Coruña and Vigo. The influences of the backward trajectories and meteorological parameters on air pollutant concentrations were considered during the studied period. The temporal trends indicate that the concentrations of almost all species steadily decreased during the lockdown period with statistical significance, with respect to the pre-lockdown period. In this context, great reductions were observed for pollutants related mainly to fossil fuel combustion, road traffic, and shipping emissions (−38 to −78% for NO, −22 to −69% for NO2, −26 to −75% for NOx, −3 to −77% for SO2, −21% for CO, −25 to −49% for PM10, −10 to −38% for PM2.5, and −29 to −51% for eBC). Conversely, O3 concentrations increased from +5 to +16%. Finally, pollutant concentration data for 14 March to 20 April of 2020 were compared with those of the previous two years. The results show that the overall air pollutants levels were higher during 2018–2019 than during the lockdown period.

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