Atmospheric levels of PM 10 in relation to meteorological elements. Case study: Moldova Region, Romania

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Introduction
High concentrations of atmospheric pollutants have a significant impact on the health of the population, the most harmful effect being caused by particulate matter and nitrogen dioxide.Extensive research has been conducted in this direction, spanning global, regional and local level.Numerous articles have been published in specialized journals aiming to disclose the factors driving this process, and outlining the required the measures to mitigate pollution, for example, Atmospheric Chemistry and Physics, Environmental Monitoring and Assessment, Natural Hazards and Earth System Sciences, etc. ABSTRACT: The major goal of our research was to find statistically relevant mathematical correlations between PM 10 levels in the atmosphere and the meteorological complex over the Moldova -Romania region.The period of analysis was from 2009 to 2020.We worked on daily data on PM10 levels and meteorological parameters from a network of 25 monitoring stations.The correlations between PM10 levels and air temperature, solar radiation, wind and precipitation were negative.The correlations between PM10 concentrations, atmospheric pressure and relative humidity were positive.However, over the medium term (12 years), the overall climatological correlations between PM10 concentrations and meteorological elements (assessed using Pearson coefficients) had values below ±0.5.This suggests that PM10 levels are not primarily influenced by meteorological elements but rather are closely associated with daily and annual patterns of socioeconomic activities.At time intervals of a few days up to 1-2 weeks, in certain favorable geographical situations, the correlative links between PM10 levels and meteorological elements can become strong and relevant (the Pearson index has in these temporal and spatial contexts values above or below 0.9 units).

GEOREVIEW 33.1 (95-117)
There are studies published in specialized journals that cover nearly the entire Europe, addressing various topics related to: the analysis of PM10 concentrations in close correlation with chemical processes and emission sources (Grivas et al., 2004;Kiesewetter et al., 2015;Hoffer et al. al., 2020;Liaskoni et al., 2023); the identification of the relationship between climatic factors and the evolution of PM10 concentrations (Houthuijs et al., 2011;Markovic et al., 2008;Bielawska, Wardencki, 2014;Largeron, Staquet, 2016;Gašparac et al., 2020;Tsyro et al., 2022); the influence of human-related factors on the variations in the concentration of mobile dust suspension (Houthuijs et al., 2011;Gualtieri et al., 2015;Matthias et al., 2021;Borlaza et al., 2022;Gorrochategui et al., 2022) In Romania, there are studies investigating: the impact of PM10 pollution episodes on the health of Bucharest residents and the surrounding environment (Raicu, Iorga, 2009;Proorocu et al., 2014); the role of meteorological factors in the PM10 concentration regime for two monitoring stations located in the green perimeters of Bucharest (Bodor et al., 2020); air pollution with PM2.5 and respectively with PM10 in a comparative study for the city of Târgu Jiu (Căpățînă et al., 2016); the role of meteorological parameters in explaining the variations of PM10 concentrations on the territory of our country (Maco B.A. et al., 2016) ; the effects of meteorological conditions on atmospheric pollution with PM10 and on human health in the city of Brăila (Bălănică et al., 2019); In the case of Moldova Region, until 2010, there were fewer studies conducted (Gugiuman, Cotrău, 1975;Erhan, 1979), but in the recent years their number has increased.These studies were focused on the emission sources of mobile dust -PM10 and the influence of meteorological conditions on the variation of PM10 concentrations in the urban agglomerations of Suceava, Piatra Neamț, Iași, Botoșani (Lazurcă, 2015;Sfîcă et al., 2018;Roșu et al., 2020;Nistor et al., 2020).In Suceava municipality, Lazurcă, 2015 and Nistor et al., 2020 looked at the influence of meteorological factors (temperature, precipitation, humidity, atmospheric pressure, wind direction and speed) on the distribution of PM10 concentrations and air quality In Iași metropolitan area, studies (Sfîcă et al., 2018) explored the role of climatic and meteorological factors on the temporal and spatial variability of PM10 particles and the connection between thermal inversions and pollution conditions.In the case of Piatra Neamț city, Roșu et al., 2020 work showed that the excellent air quality related to the low values of PM10 concentrations increased the tourist attraction of the city.The main aim of the present study is to assess the spatio-temporal distribution of PM10 at the air quality monitoring stations across the Moldova Region and to identify the stations and intervals where the air quality index exceeds the daily limit value allowed by Law 104/2011, in close connection with climatic elements (air temperature, atmospheric pressure, relative humidity, solar radiation, wind speed).The objectives of the present work aim at i) the spatial and temporal analysis of PM10 concentrations over the period 2009 -2020 for the Moldova Region, ii) the identification of the stations and intervals in which the air quality index was average, bad and very bad and iii) highlighting the relationships between spatio-temporal distribution of PM10 concentrations and meteorological elements (air temperature, precipitation, humidity, atmospheric pressure).

Study area
Moldova region is located in the north-eastern and eastern part of Romania, close to the border with Ukraine and the Republic of Moldova.It stretches between 48°15'55.893"N and 45°22'6.52"N northern latitude and between 24°58' 16.925"E and 28°17'2.602"Eeast longitude (Figure 1, Table 1).

Methods
In general, for measuring PM10 dust, which represents the dimensional fraction of toxicological interest in the urban aerosol, 2 methods are applied: the GRAV gravimetric method (reference method) and the LSPM nephelometric method (automatic measurements, for informational purposes; the recorded exceedances can be later validated/invalidated by the gravimetric method).
The reference method for sampling and measuring PM10 is the one provided in the EN 12341 standard -"Ambient air.Standardized method for gravimetric measurement to determine the mass fraction of PM10 or PM2.5 of suspended particles".Most APM stations use the gravimetric method, but some use the nephelometric method (the data used in this study were analysed by the gravimetric one).The stations that use only the nephelometric method are: IS-3, NT-2, BC-3, GL-5.
The research methods used consisted of: i) collectiong general information about PM10 by reviewing scientific articles and publications, ii) analysing and comparatively interpreting statistical data (using the Pearson correlation coefficient, PCA -Principal Component Analysis), iii) graphically representing PM10 concentration values analongside theircorresponding quality indices and iiii) interpretating graphic and cartographic material.
These methods are essential for analysing PM10 suspended particles concentrations because they provide quantitative, real and accurate data regarding the temporal evolution and spatial distribution of air quality, particularly in relation to their causality.
The potential health negative impacts of mobile dusts in suspension, especially on individuals with asthma, cardiovascular and respiratory diseases, children, and the elderly, is directly contingent on the size of PM10 suspended particles.It is very important to follow the specific daily quality index corresponding to the pollutant PM10, in order to not exceed the limit values for the protection of human health approved in Law no.104 of June 15, 2011 (Table 2).In Romanian legislation, the specific index corresponding to PM10 was recently updated, according to Order no.1818 / 2020 (Table 2).Limit values for the protection of human health were established for the PM10 pollutant to 50 μg/m 3 -for the daily limit value and 40 μg/m 3 for the annual limit value.The specific index corresponding to PM10 is established by fitting the arithmetic mean of the hourly values, recorded in the last 24 hours (daily averages), in one of the GEOREVIEW 33.1 (95-117) concentration ranges listed in Table 2.In our study, we adopted the fitting from Table 2 according to Law 104/2011, because the analysis period overlaps this interval.
The data used in this study for PM10 pollution cover the interval between 2009 to 2020.The data were obtained from the County Environmental Protection Agencies of Moldova.According to Annex 4 of Law no.104/2011, the monitoring data quality objective regarding the minimum data capture for the 1-year averaging period is 90%.Given that the 90% capture requirement does not include data loss due to routine calibration, checks and maintenance, valid data captures of at least 75% are considered compliant (Table 3).For the studied region, it is observed that the highest percentage of valid data is reached at the stations: BC-2, BC-3, IS-1, IS-4.The lowest percentage of valid data was recorded at the stations: GL-5, GL-2, IS-2, VS-2.

Analysis of air quality in Moldova based on PM10 levels
The concentrations of suspended particles with a diameter of <10 microns in the surrounding air were assessed using the annual limit value (40 µg/m 3 ).The average annual concentrations of PM10 from the APM stations in the Moldova Region were, in general, within the legally allowed values (below 40 μg/m 3 ).The highest multiannual average (2009 -2020) was calculated for station IS-1 (41.2 μg/m 3 ).This average exceeds the allowed annual limit value.Throughout the studied interval, the IS-1 station (traffic-type) had values above or close to the annual allowed limit, due to the intense road traffic on the Stone Bridge and railway traffic (the railway tracks pass under the bridge).Other high multi-year averages were calculated for stations IS-2 (32.5 μg/m 3 ), IS-6 (32.1 GEOREVIEW 33.1 (95-117) μg/m 3 ), SV-2 (31.6 μg/m 3 ) which are traffic-type stations or industrial.The lowest multiannual averages of atmospheric levels of PM10 were calculated for the EM-3 (16.9 μg/m 3 ; cross-border station, located at the altitude of 910 m) and the stations in the city of Galati: GL-2 (14.9 μg /m 3 ), GL-3 (16.7 μg/m 3 ) and GL-4 (17.5 μg/m 3 ) (after the year 2000, the activity of the Arcelor Mittal Galati steel plant was constantly reduced, the production gradually decreased and thus, the pollution level were drastically reduced https://ro.wikipedia.org/wiki/Combinatul_siderurgic_din_Gala%C8%9Bi) (Table 4).In the Moldova Region, between 2009 and 2020, the highest monthly averages of PM10 concentrations were registered during the cold season (October -March), while the lowest averages were visible for the spring and summer months (May -August).Industrial-type APM stations recorded relatively high and uniform PM10 values throughout the year, with a maximum during January or October (approx.35-40 μg/m 3 ) and a minimum during May -July.At urban-type and traffic-type APM stations, PM10 concentrations had variable values during the year, with a maxima during March and October -November and a minima during summer months.At the rural APM stations, the monthly averages show little variability throughout the year, between 15-25 μg/m 3 .The variation of PM10 mobile dust concentrations was also reduced at the EMEP type APM station (EM-3), but the average monthly values were lower throughout the year, when compared to those from the rural stations.Suburban APM stations recorded monthly averages of mobile dust, intermediate in value, between urban and rural all year round (over 25 μg/m 3 ).the atmosphere thermal stratification and temperature inversions can greatly influence the pollutants accumulation.An important factor is represented by the cars that transits the central parts of the cities the national roads outside the city and the routes that transit the villages.During the day, the period when the traffic is high is between 07:30 and 19:00, which generates high values of the PM10 particles concentration.On Saturdays and Sundays, the traffic is reduced and thus PM10 particles register the lower values -, an interval also known as the "weekend effect" (Nistor et al., 2020).

Frequency of quality indices related to PM10 levels 4.2.1. Multiannual frequency of air quality indices related to PM10
The highest frequencies of quality indices 4 -6 (average, bad and very bad) were recorded at the stations in the Iaşi metropolitan area (IS-1, IS-2, IS-6, IS-5).The lowest frequencies were recorded in the Galati metropolitan area and the EM-3 Poiana Stampei station (Figure 3).

Multi-year frequency of PM10-related air quality indices focused on quality indices of classes 6, 5 and 4
The population well-being is at risk when the particular daily quality index corresponding to suspended particles PM10 falls into classes 4 (medium), 5 (bad) and 6 (very bad).The analysis of the frequency of quality indices 6 (very bad), 5 (bad) and 4 (average) was carried out based on the station types (EMEP, industrial, traffic, urban, suburban, rural).This approach was necessary because depending on the station location there are different sources and regimes of the PM10 pollutant and the correlations what appear between the spatio-temporal distribution of PM10 concentrations and meteorological elements (air temperature, precipitation, humidity, atmospheric pressure) (Table 5).
The maximum values of PM10 concentrations occur at industrial or traffic type stations, in the cold season months, under the conditions of an anticyclonic synoptic circulation or a high pressure field, against the background of accentuated thermal inversions, which generate stable, but cold and frosty weather (Grant from the Ministry of Research, Innovation and Digitization, CNCS-UEFISCDI, project number PN-III-P1-1.1-TE-2021-0882,within PNCDI III).
GEOREVIEW 33.1 (95-117) EMEP type station.At station EM-3 the frequency of average and bad quality indices is low registering 10.3% and 0.3%.Between 2009-2020, there were no cases in which PM10 dust concentrations exceeded 100 µg/m³, the maximum value recorded was 63.1 µg/m³.Industrial stations.The highest cumulative average frequencies, bad and very bad quality indicators were recorded at SV-2 Suceava station (43.9%), due to the industrial activity of the company Romcarton and Ambro SA Suceava (mercaptan pollution -these pollutants are added in the combustible gases, as they helps to signal the accidental loss of gas; they have an extremely unpleasant, suffocating, unbearable smell, which affects the respiratory tract), Pro -Metal Mechanical Processing Suceava, Suceava Wastewater Treatment Plant, SC Termica SA etc. (Table 5).
At the NT-3 station, the highest frequencies were recorded for specific indicators of average, bad, and very bad air quality (540 cases, respectively 30%), due to the industrial activity of the Heidelberg Cement Factory.In Moldova Region, one of the maximum values of the PM10 pollutant was recorded here (177.2 µg/m³ -February 1, 2017).The industrial activities on the Bacău industrial platform, even if in the last ten years the production decreased (SC Sofert SA, SC Letea SA, Thermoenergy Group SA) influence the air quality.At the BC -2 station a number of 769 cases were registered for the average, bad and very bad quality index and a frequency of 21.3%.At the BC -3 station, 591 cases with an average specific index, 106 cases with a bad specific index and 13 GEOREVIEW 33.1 (95-117) cases with a very bad specific index.These values are due to the emissions of pollutants: phenols, chlorine, hydrochloric acid (SC CAROM SA).The maximum value of 205.4 µg/m³ of the pollutant PM10 was recorded at this station on December 25, 2009.The cumulative frequency of 26.6% for average, bad and very bad quality indices at the IS-3 station is due to traffic from proximity, home heating in personal or centralized system, constructions and demolitions and reduced industrial activities: SC Fortus SA, SC CET SA, SC Ceramica SA, SC Moldomobila SA etc.The maximum value of PM10 concentration at the IS-3 station exceeded 200 µg/m³ (231.5 µg/m³ on November 5, 2015).
Traffic type stations.At the IS-1 traffic type station, the highest cumulative frequencies were recorded for the specific average, bad and very bad air quality indices (72.7%), due to the heavy traffic that occurs in the vicinity of the Stone Bridge, and other anthropogenic activities including the home heating.To this one can add the relief configuration, the Bahluiului Valley, which favours the stagnation of cold air masses and the accumulation of PM10 suspended dust concentrations.This is the only station in Moldova where the number of cases and the cumulative frequency of bad and very bad air quality indices exceeds 20% (Table 5).At the SV-3 station, high cumulative frequencies of average, bad and very bad quality indices were recorded (35.7%) due to the heavy road traffic on the European road E85 and the location in the Siretului valley.Cumulative frequencies of average, bad and very bad quality indices of 44.4% at station IS -6 are due to industrial activities (more than 42 enterprises) across the Republic of Moldova border in the city of Ungheni, which is the 6th city as size in this country and the largest in the Central Moldova Development Region.At traffic-type APM stations, the maximum values of PM10 concentrations exceeded the value of 100 µg/m³ (IS-1 -285.7 µg/m³ on March 4, 2011, IS-6 -170.4 µg/m³ on September 19 2018, SV-3 -124.0 µg/m³ on January 10, 2013).These elevated PM10 levels were associated with specific meteorological conditions from a meteo-synoptic perspective, manifesting on both a local and continental scale, and they coincided with pronounced thermal inversions.
Urban stations.Station BT -1 had a higher number of cases for average, poor and very poorquality indices than the other stations.This is due to the fact that this station attains the longest record of valid data (3242) compared to SV -1 (2753), NT -1 (2803), VS -1 (2716).The high frequency of these indices is due to human activities (demolitions, new constructions, sweeping, household shaking, fires), accidental fires, home heating in individual or centralized system, to which we can add gas emissions resulting from the combustion process (Nistor et al., 2020).At urban stations, the frequencies of average, bad and very bad specific indices increase from October to March, as air temperature values decrease and atmospheric pressure increases.These meteorological conditions favour the accumulation and stagnation of PM10 concentrations at ground level.Also, during this period, home heating systems are also activated (Table 5).
Suburban stations.The higher cumulative frequencies of the average, bad and very bad indices recorded at the IS -5 Tomești station (22.7%) were primarily due to the fact that most houses are heated in individual system, such as wood or coal stoves.None of the counties of Moldova exceeded the threshold of 50% for houses with centralized heating.However, Iaşi County shows the highest percentage of 47%.In terms of air quality protection, the situation is critical, because winter thermal regime is more severe in Moldova (Table 5).The maximum value of PM10 concentrations exceeded 150 µg/m³ at IS-5 station (182.8 µg/m³ was recorded on November 5, 2015, a high value similar to that recorded at IS-3 station.This happened under a southeasterly anticyclonic circulation and also considering the configuration of the relief -Bahlui Valley which favours the stagnation of air masses.Wood stoves emit between 10 and 180 grams of carbon monoxide for every kilogram of wood burned.Carbon monoxide has serious effects on health because it mixes with the blood in the body (Report-RO-18-june-01.pdf(fppg.ro).Wood burning releases in the atmosphere aldehydes, polycyclic aromatic hydrocarbon compounds (known as GEOREVIEW 33.1 (95-117) PAHs), volatile compounds (https://www.meteorologiaenred.com/).Coal is considered the most polluting source.Coal burning it releases suspended dust, carbon dioxide, nitrogen oxides, sulphur dioxide, persistent organic pollutants and a series of heavy metals -mercury, lead, arsenic, cadmium, with a negative impact on people's health and the surrounding environment.
Rural stations.At IS-4 station, there were high cumulative frequencies for specific average, bad and very bad air quality indices of 43.2%.This happens because air transports pollutants from the fuel combustion to the high atmosphere (upper troposphere and stratosphere)(Table 5).According to the statistical data provided by INSSE, the highest passenger flows and the highest number of flights were recorded in the Iași, airport followed by Bacău and Suceava (https://insse.ro/cms/ro/content/transportul-aeroportuar-of-passengers-%C5%9Fi-m%C4%83rfuri-%C3%AIn-the-year-2022).This determined the relocation of the continuous air quality monitoring station (IS-4) from the centre of the city (Copou district), closer to airport (Aroneanu town).The main aim of this relocation was to evaluate the influence of air traffic on atmospheric pollution with pollutants resulting from the combustion process.

IV.2.3. Monthly / seasonal frequency and station types of air quality indices related to PM10 focused on quality indices of classes 6, 5 and 4
The frequency of daily air quality indices reveals that traffic-type stations record high values at bad and very bad indices and low values at excellent and very good indices due to pollutants emitted by road traffic.This occurred especially when car traffic was intense during morning, at noon and in the afternoon the population resumes its activity during the week -students at school, the movement of people to work, in the period before the holidays).The Car tires during the start/stop of the car moves the dust from the street and thus, constitute an additional source of emission of PM10 mobile dust.The substances emitted by road traffic are numerous: mobile dust, carbon monoxide, nitrogen oxides, lead, aldehydes, carcinogenic organic substances (dioxin, benzopyrene), carbon dioxide, volatile organic compounds.By burning one litter of gasoline, 275 g of carbon monoxide, 13.5 g of nitrogen oxides, 24 g of aromatic hydrocarbons with a carcinogenic effect, 1.5 g of suspended particles are eliminated.(Dițoiu and Holban, 2005).EMEP and rural stations record low values of bad and very bad air quality indices, due to low road traffic (Figure 4).
In the cold season, the average, bad and very bad quality index values are high at all APM stations in Moldova.The very bad index is registered in the warm season (April -September) only at traffic type stations (0.22%).The bad index has values above 5% in the cold season (October -March) at industrial, traffic, urban and suburban type stations with values between 11% at traffic type stations and 6% at suburban type stations.In the cold season, the average quality index is higher at all APM stations, and the cumulative frequency of average, bad and very bad indices exceed 30% at traffic (42.3%), industrial (36.1%), urban type stations (35.5%) and suburban (29.9%).In the warm season, cumulative frequencies of over 80% of the excellent, very good and good indices were recorded at the APM stations in Moldova, with the exception of the traffic type stations (at the EM-3 station, the cumulative frequency of indices 1 -3 is 96 %).
All these values are closely related to the configuration of the relief (traffic and industrial stations are located in the valley) and to the synoptic situations.From a climatological point of view, periods of great atmospheric stability have been observed in the region of Moldova, when the phenomenon of fog appears, which is frequently accompanied by precipitation.Temperature inversions of thermal origin (nocturnal radiation inversions) are responsible for the greater number of foggy days, which correlated with combustion emissions lead to increases in PM10 concentrations in the morning and evening.It was observed that in the autumn and early winter months (October, November and December) there were persistent thermal inversions that led to the maintenance of high levels of PM10 suspended particle concentrations in the atmosphere.On the other hand, against the background of the production of significant episodes of precipitation in the cold season (precipitation in the form of snow contributed even more to the removal of mobile particles from the atmosphere, having a depolluting role) the average values of PM10 concentrations were very low (Nistor et al., 2020).
The pollution problems faced by the study region also start from the high frequency of days with atmospheric calm at the local level.Thus, during these days, the dispersion of pollutants is very low, the calm being associated with the lack of precipitation for long periods of time.

Frequency by month and station types of air quality indices related to PM10 focused on quality indices of classes 6 and 5
The value of 50 μg/m 3 of PM10 concentrations represents the daily limit value for the protection of human health (Law 104/2011).Above this value, the air is considered bad (50 -100 μg/m 3 ) or very bad (above 100 μg/m 3 ).Frequencies of very poor-quality index above 1% were recorded at IS-1, SV-2 and IS-3 stations and frequencies of poor quality index above 11% were recorded at IS-1, IS-2 and IS stations -6.The highest cumulative values of the bad and very bad indices exceeded 10%, with percentages ranging from 20.3% in IS-1 to 10.0% in SV-2.Very low frequencies of the very bad index were recorded at stations EM-3, GL-1, GL-2, GL-3, GL-4, GL-5, VS-2, VN-1 (0%) (Table 6).
The frequency of the specific air quality index bad and very bad was due to several causes: heavy traffic responsible for exhaust gas emissions for stations IS-1, IS-6, SV-3 and GL-1, industrial activities for stations IS-3, SV-2, human activities (demolition,street sweeping,household sweeping,smoking), accidental fires, home heating in own or centralized system (heating with individual apartment gas centrals and especially with stoves fuelled with wood, coal, pieces of furniture, PAL, PFL, especially in the cold period of the year, starting from October -November until April).In this interval, due to the combustion process of fuels, the concentrations of pollutants PM10, CO, NOX, SO2 increase, for urban type stations (BT-1, IS-2, NT-2, SV-1) and suburban type (IS-5).
The cumulative monthly values of the bad and very bad quality indices reveal the following aspects: for the urban type stations, the months with the highest production frequencies of indices 5-6 (over 5%) were recorded in the months of the cold season, the exception being station IS-2, where they occurred almost all year (less in the months of June and July), due to the high density of blocks, buildings and institutions in the centre of cities and the human activities in close correlation with them (home heating in own or centralized system, car / rail traffic, economic, social and cultural activities) ; -for the industrial type stations, the months with the highest frequencies of indices 5-6 were those of the cold season (January, February, March, October, November, December), because GEOREVIEW 33.1 (95-117) most of the industrial type stations are located in the valleys of some rivers ( Suceava, Bahlui, Siret, Trotuș, Bicaz, Bârlad), where the role of meteorological conditions is important (atmospheric calm and foggy conditions favour the accumulation of pollutants on the soil surface), thus determining high concentrations of PM10 pollutants;  16.0 6.1 5.9 3.9 0.0 0.0 0.0 0.0 0.0 0.9 3.3 12.9 4.1   1.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.9 0.6 1.5 0.6 for the traffic type stations, high frequencies for air quality indices 5-6 occurred all year round (except for the SV-3 station where they were recorded only in the cold season), due to the inadequate technical condition of the transport routes (deficit investments in road infrastructure) from the Moldova Region, combined with the delay of spring cleaning actions, against the background of unfavourable weather conditions, which led to the recording of high values of PM10 concentrations; -for the suburban type stations, the months with the highest frequencies of indices 5-6 were those of the cold season, because in the counties of Moldova, most homes were heated in an individual system (wood stoves, coal, gas plants), and in winter the thermal regime is more severe than other areas of Romania, affecting the air quality regime; -in the case of rural type stations, the highest frequencies for indices 5-6 were recorded in the months of January, February, October and November, due to the activities specific to the autumn season around households and in the fields (burning of vegetable remains, garbage ), heating of homes (central plants and stoves on wood, pellets, coal, gas) and transport activities (IS-4 station is located near the Iași International Airport, for monitoring pollutants from the burning of fuels used by airplanes).

Correlations between PM10 concentration and meteorological elements
The Pearson correlation coefficient is often used to measure the statistical relationship and association between different time series of analyzed elements (in our case PM10 and GEOREVIEW 33.1 (95-117) meteorological elements).The Pearson correlation coefficient provides us with valuable information regarding certain indicators we track (https://btprovider.com/ro/coeficient-corelatiepearson-tableau/).For example, we can measure how close the connection is between the concentration levels of a pollutant (PM₁₀, NO₂, CO, O₃) and the daily values of a meteorological element (air temperature, atmospheric pressure, atmospheric precipitation, relative humidity, solar radiation, speed the wind).The relationship of direct or inverse proportionality between these two components is given by the sign of the correlation index.The closer the value of the Pearson correlation coefficient is to 1 (in absolute value), the greater the "intensity" of the linear relationship between the 2 variables.The Pearson correlation coefficient (r) can vary between the thresholds of -1 and 1, and its values can be interpreted using the information in Table 7 (https://phys.ubbcluj.ro/).
Table 7 Thresholds of r and the type of correlation indicated by r in different situations.
Correlative links of PM10 concentrations with meteorological elements are supported only at certain stations by Pearson correlation coefficients, which have values close to ±1.Most frequently, these coefficients have values less than ± 0.5, indicating that PM10 levels from the atmosphere are not strongly influenced by meteorological factors, but rather by the diurnal and annual rhythm of socio-economic activities (except for the cross-border station EMEP EM-3) (Figure 5).
The correlation between PM10 and meteorological elements (air temperature, air humidity, atmospheric precipitation, atmospheric pressure, wind speed, solar radiation) can be seen from the analysis of the PCA graphs (Principal Component Analysis) as follows: -the sharp angles reflect strong positive correlations between PM10 and meteorological elements (Examples: strong positive correlative links were created between PM10 and relative humidity, respectively atmospheric precipitation -Table 8 and Figure 5 a, d, f); -the obtuse angles and especially the opposite segments with which PM10 forms an angle of 180° or close to it reflect negative correlations between PM10 and meteorological elements (Examples: strong negative correlative links were created between PM10 and wind speed, respectively global radiation -Table 8 and Figures  The correlation between PM10 concentration values and air temperature is negative and shows an inverse proportional relationship between temperature and PM10.In the hours and days when high temperature values were recorded, the levels of PM10 in the atmosphere decreased, and in the intervals with low temperature values, the concentrations of PM10 mobile dust increased.The same situation can be observed in the case of the correlation of PM10 pollutant values with solar radiation.Regarding the correlation between the concentration of PM10 mobile dust and atmospheric pressure, it is a positive one: when the atmospheric pressure is high, the concentration level of PM10 increases, and the lower the atmospheric pressure, the lower the concentration level of PM10.The same is true of the correlation between relative humidity and the pollutant PM10.The correlations between wind, precipitation and PM10 values are negative (inversely proportional): the higher the wind speed and the higher the amount of precipitation, the lower the concentrations of the PM10 pollutant (they are either dispersed by the wind or washed away by precipitation).PM10 concentrations register high values in the cold season, when the air temperature drops, the air is cold and dry and the atmospheric pressure is high, against the background of thermal inversions and also when the fog phenomenon prevails.
At each station where we analysed PCA, we were able to identify links between PM10 and physicalgeographical and socio-economic factors, resulting in different correlations.The EMEP type station is located at high altitude (910m), about 800m from the E58 road, with low economic activities (positive correlations between PM10 and relative humidity/atmospheric precipitation and negative correlations between PM10 and solar radiation and wind speed).The SV-1 urban station is in the Zamca neighbourhood, between the blocks, in the high area of Suceava city, with intense economic activities (positive correlations between PM10 with relative humidity and atmospheric The BC-2 industrial type station is in the Letea industrial area, approx.150 m from the Bistrița valley, with intense economic activities (positive correlations between PM10 with relative humidity and atmospheric pressure and negative correlations between PM10 with solar radiation, air temperature and wind speed).The IS-1 traffic station is in the Podul de Piatră area, approx.100 m from the Bahluiului valley and approx.200 m from the Podul de Piatră roundabout, at the E58 intersection with the National Road (positive correlations between PM10 with relative humidity and atmospheric precipitation and negative correlations between PM10 with wind speed).The IS-5 suburban station is in Tomești, in the Bahluiului meadow, approx.1.4 km from CET Holboca (positive correlations between PM10 with relative humidity and atmospheric precipitation and negative correlations between PM10 with solar radiation, air temperature and wind speed).The IS-4 rural station is approx.2 km N of Iasi International Airport, in the village of Aroneanu (positive correlations between PM10 with relative humidity and atmospheric precipitation and negative correlations between PM10 with solar radiation, air temperature and wind speed).
Meteorological conditions can be critical in limiting or increasing the concentration of PM10 and can influence the crossing of certain thresholds that can affect human health.

Conclusions
The annual average concentrations of PM10 from the APM stations in the Moldova Region were generally within the legally allowed values (below 40 μg/m 3 ).The highest multiannual average (2009 -2020) was calculated for station IS-1 (41.2 μg/m 3 ).This average exceeds the annual allowable limit value and throughout the studied interval at this station values above or close to the annual allowable limit were recorded due to heavy road traffic.Other high multi-year averages were calculated for stations IS-2 (32.5 μg/m 3 ), IS-6 (32.1 μg/m 3 ) and SV-2 (31.6 μg/m 3 ), traffic-type stations or industrial.The lowest multiannual averages of atmospheric PM 10 levels were calculated for EM-3 (16.9 μg/m 3 ; cross-border station, located at the altitude of 891 m) and the stations in the city of Galati: GL-2 (14.9 μg /m 3 ), GL-3 (16.7 μg/m 3 ) and GL-4 (17.5 μg/m 3 ) (after 2000, the activity of the ArcelorMittal Galați steel plant was reduced, production gradually decreased, and thus, the level of pollution decreased drastically).
The highest values of PM10 dust concentrations were recorded at industrial and traffic type stations, due to pollution sources (industrial activities, traffic) and also in the valley lanes of Suceava (SV-2), Siret (SV -3, NT-2, BC-2), Bahlui (IS-1, IS-2, IS-5), Bicaz (NT-3), Trotuș (BC-3) rivers, which due to the configuration of the relief favours the accumulation and stagnation of particles PM10.The highest monthly averages of PM10 concentration were calculated for the cold season months (mostly October, November, February, March) and the lowest were calculated for the late spring and summer months (May -August).The maximum values of PM10 concentrations occurred at industrial or traffic type stations, during the cold season, under the conditions of an anticyclonic synoptic circulation or a high-pressure field, against the background of accentuated thermal inversions, which generate stable but cold weather and frosty.
In the period 2009 -2020, the highest cumulative values of the bad and very bad indices exceeded 10%, (between 20.3% in IS-1 and 10.0% in SV-2).The highest frequencies of quality indices 4-6 (average, bad and very bad) was recorded at the stations in the Iași metropolitan area (IS-1, IS-2, IS-6, IS-5), and the smallest at the stations in the Galati metropolitan area (GL-1, GL-2, GL-3, GL-4) and at the EM-3 Poiana Stampei station.The frequency of the daily air quality indices shows that at the traffic type stations the bad and very bad indices had high values, due to the exhaust gas emissions resulting from the transport activity.
GEOREVIEW 33.1 (95-117) In the cold season, the average, bad and very bad quality index values were high at all APM stations in Moldova except EMEP and rural stations.The cumulative frequency of average, bad and very bad indices exceeded 30% at traffic (42.3%), industrial (36.1%), urban (35.5%) and suburban (29.9%) stations.The bad index recorded values above 5% (October -March) at industrial, traffic, urban and suburban stations (with values between 11% at traffic stations and 6% at suburban stations).
In the months of the warm season, the APM stations in Moldova recorded cumulative frequencies of over 80% of the excellent, very good and good quality indices, except for the traffic type stations.The very bad air quality index was recorded during April -September, only at traffic type stations (0.22%).
Correlative links of PM10 concentrations with meteorological elements are supported only at certain stations by Pearson correlation coefficients, which must have values close to ± 1.In our study, we observed that frequently, these coefficients have values less than ±0,5, indicating that the concentrations of suspended mobile particles PM10 in the atmosphere are not strongly influenced by meteorological factors, but rather by the diurnal and annual rhythm of socioeconomic activities (with the exception of the cross-border station EM-3).
The correlation between PM10 concentrations and meteorological factors is strong at the EM -3 station (because it is located at high altitude and there are no industrial, human and transport activities with major environmental impact) and much less at the industrial type stations, urban and traffic, because the anthropogenic factor is the most important.
There are negative correlations between PM10 concentrations and solar radiation, air temperature, wind speed and atmospheric precipitation.Positive correlations were established between PM10 mobile dust and atmospheric pressure, respectively relative humidity.
Research on the phenomenon of air pollution and the identification of the measures that must be implemented to maintain, protect and improve air quality has gained momentum due to the negative impact of PM10 suspended particles on air quality, human health and the natural environment.Therefore, a combined effort of stakeholders (local, regional, national), based on quantitative data obtained from research, seems to be the most effective and sustainable solution to prevent and limit the negative effects due to the increase in PM10 concentrations.

Figure 1
Figure 1 Map of the Moldova Region showing the location of APM station studied.

Figure 3
Figure 3 Frequency (%) of daily air quality indices (classes 1-6) at each individual monitoring station for the PM10 indicator in the Moldova Region (2009-2020).
Frequency (%) of daily air quality indices (classes 1-6) for each month separately for the PM10 indicator at EMEP station -EM3 -Poiana Stampei -a and at industrial type stations -b, traffic -c, urban -d, suburban -e and rural -f from Moldova (2009-2020).
5 a, b, c, e); -right angles reflect the lack of correlations between PM10 and meteorological elements.

Table 1
Geographical characteristics of the of APM station locations within the Moldova Region.

Table 4
Average annual concentrations for the PM10 pollutant at APM stations in the Moldova Region (2009 -2020).
Average daily concentrations of PM10 suspended particles are directly influenced by meteorological factors including: wind direction and speed, precipitation, air temperature, atmospheric pressure, etc. and the geographical factors specific to the studied area.The types of GEOREVIEW 33.1 (95-117)

Table 6
Cumulative frequency (%) by month and types of stations of bad and very bad quality index at APM stations inMoldova (2009Moldova (  -2022)).