Spatiotemporal Variations of Particulate Matter in Tirana

Air pollution regards all chains of environmental prospective. As an actualized and future issue we concentrate our efforts to set a frame on major air pollutants and their relation. The period of study rely on 15 year time interval (2012–2016) and the geographical area is focused on data retrieved from the capital, Tirana. We canalize our investigation mainly on inhalable particle and their behavior toward other particles. The goal is to establish PM10 (Particulate Matter with a diameter < 10 μm) trend based on significant associations. We develop the analytical process due to air pollution numbers which turn to be of considerable concern in the country. PM10 and Total Suspended Particulate Matter (TSPM) have different diameter but reflect the same trend line. They show strong positive correlation value with O3 and SO2 (r > 0.75). NO2 particles seem to be less (r < 0.25) involved in this interaction. AQI (Air Quality Index) is fully depended (r > 0.92) on PM10 behavior. We test also socioeconomic and meteorological parameters that produce interesting results. IDW (Inverse Distance Weight) interpolation maps resume the geographical dispersion of PM10 values. The reductive emission index retrieved from Euro standard transition for vehicle fleet develops a new situation. We generate potentially future values of PM10 emission. Predictive scenario is created, interpolation maps are the backbone of this methodology.


INTRODUCTION
Air pollutants have turned into primary factor for human health and governments recognize the fact and the importance to investigate and manage this environmental issue. These adverse health effects increase medical costs, lower workforce's productivity and undermine people's quality of life [Alkoy, 2009]. Controlling air pollution is a complex task due to multiple sources that might cause the issue. Their relevance and behavior are subject to continuous changes. To define strong or moderate association between PM 10 and other air pollutants help replenish the scenario. Also the impact that factors of different nature such as geographical area, meteorological conditions, demographic pace, etc have on air pollution lead us to overtake the right measures. The primary interest in these relationship measurements is to define direct or indirect variables close to PM 10 which show strong or moderate correlation. PM 10 refers to the particle size represented as an aerodynamic diameter (in microns) cut-off limit. PM 10 refers to a concentration of PM with an aerodynamic diameter lesser than 10 μm [Krishna, 2012]. Total Suspended Particles is the fraction sampled with high-volume samplers, approximately particle diameters < 50-100 μm [Steinar et al., 2016].
Given the rapid change in air pollution, it is necessary to determine the characteristic temporal and spatial changes in pollutants as well as their relationships with meteorological conditions, and to evaluate the changes in air quality to formulate preventative and control measures [Kuang et al., 2018]. Spatiotemporal modeling methods offer expanding opportunities to environmental research because they allow a user to display and model the spatial relationships and patterns between causes and effect when geographic distribution also temporal occurrence are part of the problem [Wang, 2008]. Air quality models are designed to create a better perspective for further decision-making and establishment of strong and healthy policies [Hysenaj, 2019]. To create predictive scenarios helps maintain the right balance through all enrolled actors [Hysenaj, 2019]. Air quality management includes monitoring and analysis of pollutant concentration, spatial distribution of pollutant concentration, and assessment of no. of environmental factors aff ected by air pollutants, health risk map [Pandey et al., 2013]. Figure 1 display the geographical areas which dispose data for air pollutants. We believe that correlation and interpolation are mathematical methodologies that help identify relation and visualize dispersion. Combined they off er a useful tool for air pollution management for better environment policies. They create predictive scenarios that forego air pollution trend. Associations help us understand reasons for variability. Our investigation concentrates on fragmenting potential parameters that may induce PM 10 emission. We use a linear regression model (LR) to study the correlation of PM 10 particles to other air pollutants. Daily variations in meteorology parameters such as temperature, relative humidity (RH), precipitation, are important factors that determine PM 10 trend. Sources of PM 10 include institutional and domestic energy according to the European Environment Agency (EEA) accounting for nearly 29% and 36% of all EU countries. Thus socioeconomic factors such as population and GDP that have direct impact on numbers of materials, goods and services play an important role for PM 10 . Geographical dispersion of air pollutants analyzed through GIS software combined with deterministic methods (IDW) produce signifi cant evaluation maps.

Correlation analysis between PM 10 and socioeconomic parameters
We observe the relationship between socioeconomic parameters and PM 10   Still they both rely on positive trend and the relationship seems to be solid.

Correlation analysis between PM 10 and meteorological parameters
Temperature is observed to have a strong negative (r = -0.876) correlation with PM 10 . We notice an increment of PM 10 concentration on winter season reaching the peak during December -January and the lowest value on August (fi gure 3a). Previous studies [Hysenaj, 2019] showed that vehicle emissions are the lead factor of PM 10 generation. The eff ects of temperature on vehicle emissions was most pronounced during the initial start-up of the vehicle (cold start phase) when the vehicle was still cold, leading to ineffi cient combustion, ineffi cient catalyst operation, and the potential for the vehicle to be operating under fuelrich conditions. RH has a weak correlation factor (r = 0.21) but still generally showed a positive association (Figure 3b). From the Hernandez G et al, 2012 study, PM 10 increase up to a threshold value of 75% RH, beyond which the correlation cease. RH aff ects the natural deposition process of PM 10 , whereby moisture particles adhere to PM 10 [German et al., 2012], accumulating atmospheric PM 10 concentration. Table 1 reports the correlation between single air pollutants with emphasiz to PM 10 and TSPM behaviour. PM 10 shows both strong positive correlation toward O 3 (r = 0.84) and SO 2 (r = 0.78) but not infl uential to NO 2 (r = 0.14). Correlation value (r = 0.77) with TSPM shows that PM particles although diff erent diametter follow the same trend not only within the same category but also with particles of diff erent nature. According to table 1 we denote that TSPM shows strong correlation with O 3 (r = 0.79) and SO 2 (r = 0.85) and weak relation with NO 2 particles (r = 0.25). The AQI, is the standardized system that state and local air pollution control programs use to notify the pub-

Correlation analysis between PM 10 and other air pollutants
where: ITSPM, IPM 10 , ISO 2 and INO 2 -Individual values of total suspended particulate matter, inhalable particulate matter, sulfur dioxide and nitrogen dioxide respectively. SSPM, SRSPM, SSO2 and SNOX -Standards of ambient air quality. Table 1 refl ects perfect correlation respectively (r = 0.96) and (r = 0.92) between AQI and PM particles.

Interpolation map methodology
We use Geographical Information System (GIS) software to assess spatial trend of air pollution across the area of interest. Tirana is located DMS Lat 41° 19' 40.6308'' N and DMS Long 19° 49' 8.4900'' E. Tirana has an urban area of about 16 square miles. The interpolation technique combined with map editor software implies analytic results of air quality dispersion. ArcMap 10.2 software is used to exploit IDW technique. IDW interpolation accords to the First Law of Geography "everything is related to everything else, but near things are more related than distant From previous studies [Hysenaj, 2019] we ranked vehicle emissions as the primary factor that induce to high values of PM 10 dispersion. An old range fl eet that mostly fall into Euro 3 standard plays a key role for air pollution issue. Based on the emission index we settle a proportionally report between (Euro 3 -Euro 4 reduce emission by 50%) and (Euro 3 -Euro 5 reduce emission by 91%). We exploit current PM 10 value emissions retrieved from monitor points distributed geographically in areas with intense vehicle activity. If applied, the reductive emission index generates potentially future values of PM 10 emission. Predictive scenario is created developing interpolated maps.
The area of study recalls a perimeter of 7.5 km and area 2.9 km 2 . Within this zone we fi nd the arterials which register every year the highest frequency of road traffi c. As we observe from

CONCLUSIONS
The aim of the study is to asses some significant air pollutants and their correlative behavior. We focus our investigation on inhalable particulate matter particles. At fi rst we conclude that regardless their diameter both PM 10 and TSPM follow the same trend toward other air pollutants. Correlation values above (r > 0.75) with O 3 and SO 2 determine a strong positive relationship. NO 2 seems to follow positive but weak interaction. Inhalable particles share a determinant role on AQI indices (r = 0.96).
The research continues on socioeconomic factors. Population and GDP are chosen as significant representative models. Based on the role that anthropogenic factors play on PM 10 emissions we checked the correlation which turned to be moderate for GDP (r = 0.55) and strong (r = 0.78) for population. Both reveal to follow positive trend line as they represent straight indices of consume of materials, goods and services.  We check meteorological parameters relative humidity and temperature. Relative Humidity has a weak correlation factor (r = 0.21). RH aff ects the natural deposition process of PM 10 accumulating atmospheric PM 10 concentration. Temperature has a strong negative correlation (r = -0.876). According to the fi nal result PM 10 concentration increase on winter season (December -January) represent the months of peak and fall on its lowest levels on summer (August). The eff ects of low temperature on vehicle emissions are related to inefficient combustion leading to the increase of PM 10 emission. Correlation methodology is an efficient way to understand behavior and association. Meanwhile through interpolation maps we improve the concept of dispersion. Interpolation as correlation, tries to create a predictive scenario based on actual data. IDW interpolation is a deterministic method based on nearby weighted locations. We exploit PM 10 data combined with Euro standard emissions coefficient to understand air pollution reaction. The experiment ends with noticeable improvement of air quality with fleet replacement.