Air Pollutants and Their Impact on Chronic Diseases—A Retrospective Study in Bucharest, Romania

Air pollution is a serious problem in Romania, with the country ranking 13th among the most polluted countries in Europe in the 2021 World Air Quality Report. Despite the recognized impact of pollutants on health, there has been a lack of large-scale studies conducted in Romania. This study investigated the impact of air pollutants on patients with chronic respiratory, cardiovascular, cerebrovascular, or metabolic diseases in Bucharest and its metropolitan area from 20 August 2018 to 1 June 2022. The daily limit values for particulate matter PM10 and PM2.5 were exceeded every month, especially during the cold season, with a decrease during the COVID-19 pandemic restrictions. A significant statistical correlation was found between the monthly average values of PM2.5 and PM10 and hospitalizations for respiratory and cardiovascular diseases. A 10 µg/m3 increase in monthly average values resulted in a 40–60% increase in admissions for each type of pathology, translating to more than 2000 admissions for each pathology for the study period. This study highlights the urgent need for national and local measures to ensure a cleaner environment and enhance public health in Romania according to international regulations. © 2023 by the authors.

Unbalanced emission reductions of different species and sectors in China during COVID-19 lockdown derived by multi-species surface observation assimilation

The unprecedented lockdown of human activities during the COVID-19 pandemic has significantly influenced social life in China. However, understanding the impact of this unique event on the emissions of different species is still insufficient, prohibiting the proper assessment of the environmental impacts of COVID-19 restrictions. Here we developed a multi-air-pollutant inversion system to simultaneously estimate the emissions of NOx, SO2, CO, PM2.5 and PM10 in China during COVID-19 restrictions with high temporal (daily) and horizontal (15 km) resolutions. Subsequently, contributions of emission changes versus meteorological variations during the COVID-19 lockdown were separated and quantified. The results demonstrated that the inversion system effectively reproduced the actual emission variations in multi-air pollutants in China during different periods of COVID-19 lockdown, which indicate that the lockdown is largely a nationwide road traffic control measure with NOx emissions decreasing substantially by ∼40 %. However, emissions of other air pollutants were found to only decrease by∼10% because power generation and heavy industrial processes were not halted during lockdown, and residential activities may actually have increased due to the stay-at-home orders. Consequently, although obvious reductions of PM2.5 concentrations occurred over the North China Plain (NCP) during the lockdown period, the emission change only accounted for 8.6 % of PM2.5 reductions and even led to substantial increases in O3. The meteorological variation instead dominated the changes in PM2.5 concentrations over the NCP, which contributed 90 % of the PM2.5 reductions over most parts of the NCP region. Meanwhile, our results suggest that the local stagnant meteorological conditions, together with inefficient reductions of PM2.5 emissions, were the main drivers of the unexpected PM2.5 pollution in Beijing during the lockdown period. These results highlighted that traffic control as a separate pollution control measure has limited effects on the coordinated control of O3 and PM2.5 concentrations under current complex air pollution conditions in China. More comprehensive and balanced regulations for multiple precursors from different sectors are required to address O3 and PM2.5 pollution in China.

Significant variation in air quality in South Indian cities during COVID-19 lockdown and unlock phases.

With the spread of COVID-19 pandemic worldwide, the Government of India had imposed lockdown in the month of March 2020 to curb the spread of the virus furthermore. This shutdown led to closure of various institutions, organizations, and industries, and restriction on public movement was also inflicted which paved way to better air quality due to reduction in various industrial and vehicular emissions. To brace this, the present study was carried out to statistically analyze the changes in air quality from pre-lockdown period to unlock 6.0 in South Indian cities, namely, Bangalore, Chennai, Coimbatore, and Hyderabad, by assessing the variation in concentration of PM2.5, PM10, NO2, and SO2 during pre-lockdown, lockdown, and unlock phases. Pollutant concentration data was obtained for the selected timeframe (01 March 2020-30 November 2020) from CPCB, and line graph was plotted which had shown visible variation in the concentration of pollutants in cities taken into consideration. Analysis of variance (ANOVA) was applied to determine the mean differences in the concentration of pollutants during eleven timeframes, and the results indicated a significant difference (F (10,264) = 3.389, p < 0.001). A significant decrease in the levels of PM2.5, PM10, NO2, and SO2 during the lockdown phases was asserted by Tukey HSD results in Bangalore, Coimbatore, and Hyderabad stations, whereas PM10 and NO2 significantly increased during lockdown period in Chennai station. In order to understand the cause of variation in the concentration of pollutants and to find the association of pollutants with meteorological parameters, the Pearson correlation coefficient was used to study the relationship between PM2.5, PM10, NO2, and SO2 concentrations, temperature, rainfall, and wind speed for a span of 15 months, i.e., from January 2020 to March 2021. At a significant level of 99.9%, 99%, and 95%, a significant correlation among the pollutants, rainfall had a major impact on the pollutant concentration in Bangalore, Coimbatore, Hyderabad, and Chennai followed by wind speed and temperature. No significant influence of temperature on the concentration of pollutants was observed in Bangalore station.

Predicting the Impact of Change in Air Quality Patterns Due to COVID-19 Lockdown Policies in Multiple Urban Cities of Henan: A Deep Learning Approach

Several countries implemented prevention and control measures in response to the 2019 new coronavirus virus (COVID-19) pandemic. To study the impact of the lockdown due to COVID-19 on multiple cities, this study utilized data from 18 cities of Henan to understand the air quality pattern change during COVID-19 from 2019 to 2021. It examined the temporal and spatial distribution impact. This study firstly utilized a deep learning bi-directional long-term short-term (Bi-LSTM) model to predict air quality patterns during 3 periods, i.e., COVID-A (before COVID-19, i.e., 2019), COVID-B (during COVID-19, i.e., 2020), COVID-C (after COVID-19 cases, i.e., 2021) and obtained the R-2 value of more than 72% average in each year and decreased MAE value, which was better than other studies' deep learning methods. This study secondly focused on the change of pollutants and observed an increase in Air Quality Index by 10%, a decrease in PM2.5 by 14%, PM10 by 18%, NO2 by 14%, and SO2 by 16% during the COVID-B period. This study found an increase in O-3 by 31% during the COVID-C period and observed a significant decrease in pollutants during the COVID-C period (PM10 by 42%, PM2.5 by 97%, NO2 by 89%, SO2 by 36%, CO by 58%, O-3 by 31%). Lastly, the impact of lockdown policies was studied during the COVID-B period and the results showed that Henan achieved the Grade I standards of air quality standards after lockdown was implemented. Although there were many severe effects of the COVID-19 pandemic on human health and the global economy, lockdowns likely resulted in significant short-term health advantages owing to reduced air pollution and significantly improved ambient air quality. Following COVID-19, the government must take action to address the environmental problems that contributed to the deteriorating air quality.

Influence of Temperature and Relative Humidity on PM2.5 Concentration over Delhi

The present study is an attempt to establish relationship between the concentrations of particulate matter especially (PM2.5) and background meteorological parameters over Delhi, India with the help of statistical and correlative analysis. This work presents the evaluation of air quality in three different locations of Delhi. These locations were selected to fulfil the characteristics as residential, industrial and background locations and performed the analysis for pre and post covid-19, i.e. for 2019 and 2021. The outcome of the study shows that the meteorological parameters have significant influence on the PM2.5 concentration. It was also found that it has a seasonality with low concentration in the monsoon season, moderate in the pre-monsoon season and high during the winters and post-monsoon seasons. However, the statistical and correlative study shows a negative relation with the temperature during the winter, pre-monsoon and post-monsoon and has a positive correlation during the monsoon season. Similarly, it also has been observed that the concentration of PM2.5 shows strong negative correlation with temperature during the high humid conditions, i.e. when the relative humidity is above 50%. However, a weak correlation with ambient temperature has been established during the low humidity condition, i.e. below 50%. The overall study showed that the highest PM2.5 pollution has been observed at residential location followed by industrial and background. The study also concluded that the seasonal meteorology has a complex role in the PM2.5 concentration of the selected areas.

Performance of in-duct bipolar ionization devices on pollutant removal and potential byproduct formation in indoor environments

The COVID-19 pandemic has highlighted the importance of indoor air quality (IAQ) since SARS-CoV-2 may be transmitted through virus-laden aerosols in poorly ventilated spaces. Multiple air cleaning technologies have been developed to mitigate airborne transmission risk and improve IAQ. In-duct bipolar ionization technology is an air cleaning technology that can generate ions for inactivating airborne pathogens and increasing particle deposition and removal while without significant byproducts generated. Many commercial in-duct ionization systems have been developed but their practical performance on pollutant removal and potential formation of byproducts have not been investigated comprehensively. The results in this study showed that the in-duct bipolar ionization technology can significantly improve the particle removal efficiency of the regular filter, while no significant ozone and ion were released to the indoor air. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Removal of chloroquine from the aqueous solution by adsorption onto açaí-based biochars: Kinetics, thermodynamics, and phytotoxicity

This study aimed at emerging contaminant chloroquine (CQN) removal, widely used in the COVID-19 pandemic through adsorption and employing a low-cost activated biochar from açai fruit endocarp. Two different adsorbents from the same precursor were applied. The first (CAA) was activated at a high temperature using ZnCl2, and the second (CA) was obtained by physical activation. The adsorbents were characterized through BET, FTIR, DRX, TG/DTG, and SEM. The results showed that zinc chloride activation furnished a material with a high specific surface area (SBET) and pore volume of 762 m2 g−1 and 0.098 cm3 g−1, respectively. Adsorption kinetics and isotherm were best adjusted through the pseudo-second-order (PSO) and Freundlich for both biochars. The process was thermodynamically favorable, occurring spontaneously without energy request. Additionally, the maximum adsorption capacity for CQN was 15.56 and 40.31 mg g−1 for CA and CAA, respectively, in pH 6.84, at a temperature of 25 °C, 50 mL solution and with 0.05 and 0.02 g of adsorbent. Those results are congruent with the literature showing the versatility of the material and the efficiency of the applied adsorption process.

Two years of volatile organic compound online in situ measurements at the Site Instrumental de Recherche par Télédétection Atmosphérique (Paris region, France) using proton-transfer-reaction mass spectrometry

Volatile organic compounds (VOCs) have direct influences on air quality and climate. They indeed play a key role in atmospheric chemistry as precursors of secondary pollutants, such as ozone (O3) and secondary organic aerosols (SOA). In this respect, long-term datasets of in situ atmospheric measurements are crucial for characterizing the variability of atmospheric chemical composition, its sources, and trends. The ongoing establishment of the Aerosols, Cloud, and Trace gases Research InfraStructure (ACTRIS) allows implementation of the collection and provision of such high-quality datasets. In this context, online and continuous measurements of O3, nitrogen oxides (NOx), and aerosols have been carried out since 2012 at the SIRTA (Site Instrumental de Recherche par Télédétection Atmosphérique) observatory, located in the Paris region, France. Within the last decade, VOC measurements were conducted offline at SIRTA, until the implementation of real-time monitoring which started in January 2020 using a proton-transfer-reaction quadrupole mass spectrometer (PTR-Q-MS).The dataset acquired during the first 2 years of online VOC measurements provides insights into their seasonal and diurnal variabilities. The additional long-term datasets obtained from co-located measurements (NOx, aerosol physical and chemical properties, meteorological parameters) are used to better characterize the atmospheric conditions and to further interpret the obtained results. Results also include insights into VOC main sources and the influence of meteorological conditions and air mass origin on their levels in the Paris region. Due to the COVID-19 pandemic, the year 2020 notably saw a quasi-total lockdown in France in spring and a lighter one in autumn. Therefore, the focus is placed on the impact of these lockdowns on the VOC variability and sources. A change in the behaviour of VOC markers for anthropogenic sources was observed during the first lockdown, reflecting a change in human activities. A comparison with gas chromatography data from the Paris city centre consolidates the regional representativity of the SIRTA station for benzene, while differences are observed for shorter-lived compounds with a notable impact of their local sources. This dataset could be further used as input for atmospheric models and can be found at 10.14768/f8c46735-e6c3-45e2-8f6f-26c6d67c4723 (Simon et al., 2022a).

Naphtho[2,1-b]furan derived triazole-pyrimidines as highly potential InhA and Cytochrome c peroxidase inhibitors: Synthesis, DFT calculations, drug-likeness profile, molecular docking and dynamic studies

Napthofuran and its fused heterocyclic derivatives evaluated with varied biological activity functional groups comprise an important class of compounds for new chemical entities. We here in reporting synthesis of new 3-(4-substituted phenyl)naphtho[1′,2′:4,5]furo[2,3-e][1,2,4]triazolo[4,3-c]pyrimidines 6(a-f). Structures of the newly synthesized compounds were confirmed by making use of spectroscopic techniques like IR, NMR and Mass. The DFT calculations were taken for the selected molecules using B3LYP hybrid functional with a 6–31+G (d, p) all-electron basis set using the Gaussian 09 package. The bioactivity predictions were evaluated for the synthesized compounds. The In vitro biological activities were reported for the all compounds 6(a-f). The compound 6a showed high activity of anti-TB and antioxidant activity with at MIC 1.6 μg/ml and at percentage of inhibition (72.54±0.21) at 10μg/ml respectively. The compound 6f (73.21±0.11) showed antioxidant activity better than standard drug BHA (71.32±0.13) at 10 μg/ml. Furthermore, the docking studies for the newly synthesized molecules were carried out by Auto dock software with proteins InhA (4TZK),Cytochrome c peroxidase (2 × 08) and protease (Mpro) of SARS-CoV-2 Omicron (PDB ID: 7TOB). All the compounds showed a strong binding affinity for the docked proteins. The outcome of docking results showed that compound 6ahad excellent binding energies -10.8, -9.4, and -9.0 kcal/mol with 4TZK, 2 × 08, and 7TOB respectively. Lastly, the protein stability, fluctuations of APO-Protein, protein-ligand complexes were investigated through Molecular Dynamics (MD) simulations studies using Desmond Maestro 11.3 and potential lead molecules were identified. © 2023

Air pollution with pm10 for the municipality of Burgas and the monthly cases of disease with COVID 19, 2021

An important environmental problem for the Municipality of Burgas is the relatively high levels of PM10 pollution. Particulate matter PM10 is defined as the fraction of particles with an aerodynamic diameter smaller than 10 pm. The article provides statistical processing and evaluation of daily data on the concentration of PM10 in the air by quarters fix Burgas, 2021. A histogram of the frequency distribution of concentrations by quarters was prepared. A regression model for calculating the monthly concentrations in the atmospheric air is derived The tests and inspections performed show that the performed modelling is suitable for evaluation, analysis and forecast. Air pollution harms human health and the environment. Exposure ID air pollution is associated with a wide range of acute and chronic health effects, ranging from irritating effects to death From the end of 2019 until now in the world, Europe and in particular Bulgaria is raging a dangerous respiratory disease known as COVD19. The average monthly new cases of COVD19 for Burgas were assessed, as well as the respective maximum and minimum monthly values. A qualitative assessment of the relationship between the monthly concentrations of PM10 and the incidence of COVID19 was made.

Impacts of environmental policy uncertainty on urban environmental pollutant emissions in China: a study based on textual analysis

How to accelerate the clean use of fossil energy and promote the transformation and upgrading of energy structure is an important challenge commonly faced by countries around the world. In the post-Covid-19 era, the uncertainties faced by countries around the world are increasing and the frequency of policy adjustments in various countries is accelerating. The discharge of pollution by enterprises is significantly impacted by environmental regulatory policies. Under the carbon neutrality goal, the uncertainty of environmental policy caused by multiple political factors can directly influence the decisions made by businesses and residents, in turn, affect their confidence and expectations. However, researchers have given limited attention to measuring the environmental policy uncertainty index (EPUI). In this paper, we select 460 newspapers from the China National Knowledge Infrastructure (CNKI) newspaper database from 2001 to 2016 and use the text analysis method to directly construct China's national, provincial, and prefecture-level EPUI. The results show that China's EPUI has obvious stage characteristics and regional characteristics. By applying the Chinese city-level EPUI to the field of urban pollution reduction, we have obtained an important finding that when urban environmental policy uncertainty increases by 1%, urban industrial sulfur dioxide emission decreases by about 0.145%, and carbon dioxide emission decreases by about 0.053%. We believe that this is due to an increase in environmental policy uncertainty inhibiting the development and scaling of secondary industries.

THREE-DIMENSIONAL ANALYTICAL SOLUTION OF THE ADVECTION-DIFFUSION EQUATION FOR AIR POLLUTION DISPERSION

We develop a new analytical solution of a three-dimensional atmospheric pollutant dispersion. The main idea is to subdivide vertically the planetary boundary layer into sub-layers, where the wind speed and eddy diffusivity assume average values for each sub-layer. Basically, the model is assessed and validated using data obtained from the Copenhagen diffusion and Prairie Grass experiments. Our findings show that there is a good agreement between the predicted and observed crosswind-integrated concentrations. Moreover, the calculated statistical indices are within the range of acceptable model performance.

Prediction of atmospheric pollutants in urban environment based on coupled deep learning model and sensitivity analysis.

Accurate and efficient predictions of pollutants in the atmosphere provide a reliable basis for the scientific management of atmospheric pollution. This study develops a model that combines an attention mechanism, convolutional neural network (CNN), and long short-term memory (LSTM) unit to predict the O3 and PM2.5 levels in the atmosphere, as well as an air quality index (AQI). The prediction results given by the proposed model are compared with those from CNN-LSTM and LSTM models as well as random forest and support vector regression models. The proposed model achieves a correlation coefficient between the predicted and observed values of more than 0.90, outperforming the other four models. The model errors are also consistently lower when using the proposed approach. Sobol-based sensitivity analysis is applied to identify the variables that make the greatest contribution to the model prediction results. Taking the COVID-19 outbreak as the time boundary, we find some homology in the interactions among the pollutants and meteorological factors in the atmosphere during different periods. Solar irradiance is the most important factor for O3, CO is the most important factor for PM2.5, and particulate matter has the most significant effect on AQI. The key influencing factors are the same over the whole phase and before the COVID-19 outbreak, indicating that the impact of COVID-19 restrictions on AQI gradually stabilized. Removing variables that contribute the least to the prediction results without affecting the model prediction performance improves the modeling efficiency and reduces the computational costs.

Effects of COVID‐19 lockdown measures on nitrogen dioxide and black carbon concentrations close to a major Italian motorway

During the first half of 2020, the Italian government imposed several restrictions to limit the spread of the COVID‐19 pandemic: at the beginning of March, a heavy lockdown regime was introduced leading to a drastic reduction of traffic and, consequently, traffic‐related emissions. The aim of this study is to evaluate the effects of these restrictions on pollutant concentrations close to a stretch of the Italian A22 motorway lying in the Alpine Adige valley. In particular, the analysis focuses on measured concentrations of nitrogen dioxide (NO2) and black carbon (BC). Results show that, close to the motorway, NO2 concentrations dropped by around 45% during the lockdown period with respect to the same time period of the previous 3 years. The equivalent analysis for BC shows that the component related to biomass burning, mostly due to domestic heating, was not particularly affected by the restrictions, while the BC component related to fossil fuels, directly connected to traffic, plummeted by almost 60% with respect to the previous years. Since atmospheric concentrations of pollutants depend both on emissions and meteorological conditions, which can mask the variations in the emission regime, a random forest algorithm is also applied to the measured concentrations, in order to better evaluate the effects of the restrictions on emissions. This procedure allows for obtaining business‐as‐usual and meteorologically normalized time series of both NO2 and BC concentrations. The results derived from the random forest algorithm clearly confirm the drop in NO2 emissions at the beginning of the lockdown period, followed by a slow and partial recovery in the following months. They also confirm that, during the lockdown, emissions of the BC component due to biomass burning were not significantly affected, while those of the BC component related to fossil fuels underwent an abrupt drop.

Impact of Anthropogenic Emission Reduction during COVID-19 on Air Quality in Nanjing, China

To avoid the spread of COVID-19, China has implemented strict lockdown policies and control measures, resulting in a dramatic decrease in air pollution and improved air quality. In this study, the air quality model WRF-Chem and the latest MEIC2019 and MEIC2020 anthropogenic emission inventories were used to simulate the air quality during the COVID-19 lockdown in 2020 and the same period in 2019. By designing different emission scenarios, this study explored the impact of the COVID-19 lockdown on the concentration of air pollutants emitted by different sectors (industrial sector and transportation sector) in Nanjing for the first time. The results indicate that influenced by the COVID-19 lockdown policies, compared with the same period in 2019, the concentrations of PM2.5, PM10, and NO2 in Nanjing decreased by 15%, 17.1%, and 20.3%, respectively, while the concentration of O3 increased by 45.1% in comparison;the concentrations of PM2.5, PM10 and NO2 emitted by industrial sector decreased by 30.7%, 30.8% and 14.0% respectively;the concentrations of PM2.5, PM10 and NO2 emitted by transportation sector decreased by 15.6%, 15.7% and 26.2% respectively. The COVID-19 lockdown has a greater impact on the concentrations of PM2.5 and PM10 emitted by the industrial sector, while the impact on air pollutants emitted by the transportation sector is more reflected in the concentration of NO2. This study provides some theoretical basis for the treatment of air pollutants in different departments in Nanjing.

Measurement of Phthalates in Settled Dust in University Dormitories and Its Implications for Exposure Assessment

Six phthalates: dimethyl phthalate (DMP), diethyl phthalate (DEP), di(n-butyl) phthalate (DnBP), butyl benzyl phthalate (BBzP), di(2-ethylhexyl) phthalate (DEHP), and di(n-octyl) phthalate (DOP) in settled dust on different indoor surfaces were measured in 30 university dormitories. A Monte Carlo simulation was used to estimate college students' exposure via inhalation, non-dietary ingestion, and dermal absorption based on measured concentrations. The detection frequencies for targeted phthalates were more than 80% except for DEP (roughly 70%). DEHP was the most prevalent compound in the dust samples, followed by DnBP, DOP, and BBzP. Statistical analysis suggested that phthalate levels were higher in bedside dust than that collected from table surfaces, indicating a nonuniform distribution of dust-phase phthalates in the sleep environment. The simulation showed that the median DMP daily intake was 0.81 μg/kg/day, which was the greatest of the targeted phthalates. For the total exposures to all phthalates, the mean contribution of exposures during the daytime and sleeping time was 54% and 46%, respectively.

Impact of COVID-19 Lockdown on Ambient Air Quality in the Southwest Coastal Urban Regions of India

The rapid growth of urban areas and population as well as associated development over recent decades have been a major factor controlling ambient air quality of the urban environment in Kerala (India). Being located at the southwestern fringe of the Indian peninsula, Kerala is one of the regions that has been significantly influenced by the activities in the Indian Ocean. The present study focuses on the effect of the COVID-19 lockdown (in 2021) on ambient air quality in the selected coastal metropolitan areas of Kerala. Although previous research studies reported improvement in ambient air quality in Kerala during the lockdown period, this study demonstrates the potential of onshore transport of air pollutants in controlling the air quality of coastal urban regions during the lockdown period. Data from the ambient air quality monitoring stations of the Kerala State Pollution Control Board in the urban areas of Thiruvananthapuram (TM), Kollam (KL), Kozhikode (KZ), and Kannur (KN) are used for the analysis. Temporal variation in the concentration of air pollutants during the pre-lockdown (PRLD), lockdown (LD), and post-lockdown (PTLD) periods (i.e., 1 March to 31 July) of 2021 is examined to assess the effect of lockdown measures on the National Air Quality Index (AQI). Results indicate a significant decline in the levels of air pollutants and subsequent improvement in air quality in the coastal urban areas. All the effect of lockdown measures has been evident in the AQI, an increase in the concentration of different pollutants including CO, SO2, and NH3 during the LD period suggests contributions from multiple sources including onshore transport due to marine traffic and transboundary transport. © 2023, The Author(s) under exclusive licence to Institute of Earth Environment, Chinese Academy Sciences.

Recent Urban Issues Related to Particulate Matter in Ploiesti City, Romania

The present work aimed to assess the ambient levels of air pollution with particulate matter for both mass concentrations and number of particles for various fractions in Ploiesti city during the lockdown period determined by the COVID-19 pandemic (March–June 2020). The PM10 continuously monitored data was retrieved from four air quality automatic stations that are connected to the Romanian National Network for Monitoring Air Quality and located in the city. Because no other information was available for other more dangerous fractions, we used monitoring campaigns employing the Lighthouse 3016 IAQ particle counter near the locations of monitoring stations assessing size-segregated mass fraction concentrations (PM0.5, PM1, PM2.5, PM5, PM10, and TPM) and particle number concentration (differential Δ) range between 0.3 and 10 microns during the specified timeline between 8.00 and 11.00 a.m., which were considered the morning rush hours interval. Interpolation maps estimating the spatial distribution of the mass concentrations of various PM fractions and particle number concentration were drawn using the IDW algorithm in ArcGIS 10.8.2. Regarding the particle count of 0.5 microns during the lockdown, the smallest number was recorded when the restriction of citizens' movement was declared (24 March 2020), which was 5.8-times lower (17,301.3 particles/cm3) compared to a common day outside the lockdown period (100,047.3 particles/cm3). Similar results were observed for other particle sizes. Regarding the spatial distribution of the mass concentrations, the smaller fractions were higher in the middle of the city and west (PM0.5, PM1, and PM2.5) while the PM10 was more concentrated in the west. These are strongly related to traffic patterns. The analysis is useful to establish the impact of PM and the assessment of urban exposure and better air quality planning. Long-term exposure to PM in conjunction with other dangerous air pollutants in urban aerosols of Ploiesti can lead to potential adverse effects on the population, especially for residents located in the most impacted areas.