Statistical study and physicochemical characterization of particulate matter in the context of Kraków, Poland

Journal Pre-proof Statistical Study and Physicochemical Characterization of Particulate Matter in the Context of Kraków, Poland Piotr Kunecki, Wojciech Franus, Magdalena Wdowin PII:

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https://doi.org/10.1016/j.apr.2019.12.001

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Atmospheric Pollution Research

Received Date: 11 September 2019 Revised Date:

1 December 2019

Accepted Date: 1 December 2019

Please cite this article as: Kunecki, P., Franus, W., Wdowin, M., Statistical Study and Physicochemical Characterization of Particulate Matter in the Context of Kraków, Poland, Atmospheric Pollution Research, https://doi.org/10.1016/j.apr.2019.12.001. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.

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Statistical Study and Physicochemical Characterization of Particulate Matter in the Context of Kraków, Poland

3 Piotr Kuneckia1*, Wojciech Franusb2, Magdalena Wdowina3

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a

Mineral and Energy Economy Research Institute, Polish Academy of Sciences, Wybickiego 7A, 31-261 Kraków, Poland b Lublin University of Technology, Nadbystrzycka 40, 20-618, Lublin, Poland 1

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[email protected] 2 [email protected] 3 [email protected]

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*Corresponding author: Piotr Kunecki, e-mail: [email protected] Wybickiego 7a, 31-261 Kraków, Poland

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Telephone number: +48126171614

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'Declarations of interest: none'.

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KEYWORDS

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particulate matter; statistical analysis; physicochemical analysis; Airly platform; anti-smog

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mask filters

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ABSTRACT

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This paper presents the results of a statistical study and physicochemical characterization of

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PM in the context of Kraków, Poland. A statistical study of PM10 and PM2.5 pollution was

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carried out using data collected from five sensors covering the area of Airly, part of a platform

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with over 3,000 PM sensors located in 15 European countries. The relationship between PM

26

properties, air temperature, humidity and atmospheric air pressure was tested using the

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Shapiro Wilk test and by creation of a Tau Kendall correlation matrix. Samples of PM

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collected on anti-smog mask filters were subjected to physicochemical analyses using a

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scanning electron microscope equipped with a chemical composition analysis system based

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on energy dispersive X-rays. The number of fractions and differences in the chemical 1

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composition of samples were determined. This example of PM concentration monitoring in

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Kraków may be of interest and a useful tool for raising public awareness of air pollution.

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1. INTRODUCTION

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Nowadays, air pollution (with its significant impact on human health and ecosystems) is

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one of the main global threats, causing over four million premature deaths every year.

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According to the latest air quality report by the European Environment Agency (EEA) (EEA,

38

2018), despite many efforts, emissions and concentrations have increased in many areas

39

worldwide. Further efforts are, therefore, needed to improve air quality. Particulate matter

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(PM) pollution is located in the borderland of the atmosphere, anthroposphere and biosphere

41

due to its origin and areas of impact. Atmospheric PM may belong to two groups according to

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the origin: primary and secondary contaminants. PM from the first group is emitted directly

43

into the atmosphere. Emission sources for these particles are numerous and varied. They may

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be naturally occurring (from volcanoes, conflagration of forests, sea aerosols or material of

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plant and animal origin) or anthropogenic – by-products of fuel and combustion processes

46

(especially solid fuels); branches of energy; mining; metallurgic, chemical and civil

47

engineering; and transport industries. PM from the second group is formed as a result of

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chemical reactions. PM consists of fine particles of solid or liquid matter, which, after being

49

emitted into the atmosphere, are present in suspended, dispersed form (atmospheric aerosol).

50

Currently, the term aerosol is commonly used to describe suspensions of solid and liquid

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dispersed particles in a dispersion medium (air in this case). Depending on the morphology,

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fraction, surface, shape and chemical composition of particles, aerosol is characterized by a

53

number of different properties (Colbeck and Lazaridis, 2010; Gieré and Querol, 2010; Hinds,

54

1999; Jacobson, 2002) The term PM (defined as a mixture of solid and liquid particles

55

suspended in the air) is used mainly by international agencies dealing with air pollution issues 2

56

and their impact on human health and ecosystems (WHO, 2006)(US EPA, 2009)(EEA, 2014).

57

The most important institutions concerned with air pollution are the WHO (World Health

58

Organization), EEA and US EPA (United States Environmental Protection Agency).

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Directive 2008/50/EC of the “European Parliament and of the Council of 21 May 2008

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on ambient air quality and cleaner air for Europe” (EUROPEAN PARLIAMENT AND THE

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COUNCIL, 2008) defines two types (fractions) of PM, which have a significant impact on

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human health. PM10 (and PM2.5, respectively) refers to particulate matter that passes through

63

a size-selective inlet, as defined in the reference method for sampling and measurement of

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PM10, EN 12341 (EN 14907), with a 50 % efficiency cut-off at an aerodynamic diameter of

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10 µm (or 2.5 µm, respectively) (EUROPEAN PARLIAMENT AND THE COUNCIL, 2008).

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These two types of matter are also subjected to different processes of formation and removal

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from the atmosphere, and they have a differing impact on human health due to varying

68

degrees of absorption and accumulation in the respiratory system. Depending on their origin,

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they may form separated populations: nucleation mode particles, Aitken mode particles and

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accumulation mode particles (Degórska et al., 2016; Gieré and Querol, 2010; Seinfeld and

71

Pandis, 2006).

72

Quantitative, surface and volume concentrations of PM are important features and can

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be used to describe these kinds of substances. Numerous studies performed worldwide have

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confirmed differentiating characteristics in relation to geographical location and the type of

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area. Ultrafine particles range from 70 % to 90 % of the total number of particles (Aitken

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mode particles generally constitute over 50 %). In urban areas, the average number of PM

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ranges from 10,000 to 25,000 particles/cm3. Areas in the vicinity of busy communication

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routes have a huge impact on the result, causing local increases of between 30,000 and 50,000

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particles/cm3. In developing countries, such as China and India, these concentrations reach

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over 60,000 particles/cm3. In suburban and extra-urban areas, total quantitative concentrations 3

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of PM do not usually exceed 10,000 particles/cm3. In emergency situations, hourly

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quantitative concentrations of PM may reach values of several hundred thousand particles/cm3

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(Dunn et al., 2004; Ruellan and Cachier, 2001). Surface and volume concentrations of PM

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have been subjected to studies much less often than particle numbers. The total PM in urban

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areas ranges from about 300 µm2/cm3 to 1,400 µm2/cm3, while the total volume of PM ranges

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from 10 µm3/cm3 to 90 µm3/cm3 (Stanier et al., 2004; Woo et al., 2001; Wu et al., 2008).

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When mineral dust reaches high concentrations, the volume of particles increases several

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times, up to 1,200–1,560 µm3/cm3 (Wu et al., 2008)(Shen et al., 2011). A number of studies

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confirm that the accumulation mode has the main share of surfaces (70–80 %) and volumes

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(over 80 %) of TSP (Woo et al., 2001)(Gao et al., 2009).

91

Air pollution, a pressing issue affecting human health and quality of life across the

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globe, is currently the object of research and reflection among scientists around the world.

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The problem has many facets and is highly complex. Its widespread prevalence touches many

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aspects of human life and has become a local, regional and global issue. A number of papers

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have been published in the literature on problems associated with PM presence within a range

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of human contexts. Statistics report that a growing percentage of the population works in

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office buildings worldwide, and the air quality in these indoor environments is now becoming

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a key area of research. Several sources and health effects of ambient outdoor PM pollution

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have been considered and widely described in papers (Bo et al., 2017; Lowther et al., 2019;

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Nezis et al., 2019). Several sources and health effects of ambient outdoor PM pollution was

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considered and widely described in following papers: (Hime et al., 2018)(Li et al.,

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2017)(Consonni et al., 2018). Some authors have focused their work on health effects after

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exposure to environments containing polluted air and have studied the benefits of reducing

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this kind of pollution(Manojkumar and Srimuruganandam, 2019)(Altieri and Keen,

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2019)(Ansari and Ehrampoush, 2019). 4

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AIM OF THE STUDY

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The main aim of this paper is to consider the issue of PM pollution through a statistical

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study and physicochemical characterization of particulate matter in the context of Krakow,

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Poland. An innovative approach has been used in this study in order to obtain a base for

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statistical consideration. The data come from one of the most advanced and fastest growing

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platforms used to monitor air quality across the world. Another aspect of this innovative

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approach involves using filters from anti-smog masks as a medium for studying the

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physicochemical properties of PM.

114

Total annual emissions of PM in global scale are estimated to be 12,400 Tg. Of this, 98

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% are natural emissions (the highest proportion being sea salt, mineral dust and volcano

116

emissions). Of natural emissions, 99 % are primary particles. In the case of anthropogenic

117

emissions, most come from industry and fuel combustion processes. Half of these (about 150

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Tg) are primary PMs. The second half are secondary, formed mainly as a result of

119

aerosolization precursors (NOx and SOx) as well as volatile organic compound reactions and

120

transformation (Gieré and Querol, 2010)(Andreae and Rosenfeld, 2008).

121

Global air pollution and its impact on human health is currently one of the most

122

pressing contemporary issues. According to the WHO, more than 4.2 million people

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worldwide die annually as a result of exposure to ambient (outdoor) air pollution, leading to

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strokes, heart disease, lung cancer or chronic respiratory diseases. Every year, 3.8 million

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deaths are caused as a result of household exposure to smoke from dirty cooking stoves and

126

fuel. Of the total world population, 91 % live in places where air quality does not meet WHO

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guideline limits (WHO, 2005). These data indicate the scale of the problem, which is the main

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focus of this paper. The WHO Global Ambient Air Quality Database from 2018 (WHO, n.d.)

129

lists the most polluted cities in the world, identifying 2,603 and 3,515 cities across the world

130

that were held to account for direct PM2.5 and PM10 concentrations, respectively. 5

131

Supplementary material 1 contains a list of 50 cities whose most recent direct PM2.5 and

132

PM10 measurements have the highest concentrations (annual mean). In terms of PM2.5,

133

annual mean concentrations range from 75 to 173 µg/m3 (according to data collected between

134

2009 and 2016). According to direct measurements of PM2.5 concentrations, two countries

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are definitely at the top of the list: 23 of the most polluted cities are located in China, and 14

136

are Indian cities (four from Bangladesh and three from Pakistan). Each of the following

137

countries has one city on the list: Mongolia, Saudi Arabia, Kuwait, Qatar, Uganda and

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Cameroon. Annual mean concentrations of PM10 range from 153 to 540 µg/m3 (according to

139

data collected between 2010 and 2016). Analogically (as before), taking into account only

140

direct measurements, there is definitely more diffusion of polluted cities (depending on the

141

country). India remains at the top of the list with 19 cities. Bahrain and Saudi Arabia have five

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cities on the list, and each of the following countries have three cities: Pakistan, China and

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Iran. Egypt, Kuwait and Bangladesh each have two, and the following have one city on the

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list: Bhutan, Uganda, Iraq, Ghana, Qatar and the United Arab Emirates.

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According to the latest EEA report (EEA, 2018), air pollution is recognized as the

146

principal cause of disease and premature death in Europe. Over 400,000 people die

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prematurely each year due to poor air quality and related health complications. Table 1 below

148

shows recent data on premature death rates in EU countries, caused by PM2.5. In addition to

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PM, exposure to NO2 and O3 is also indicated as being very harmful to human health.

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Table 1. Annual premature death rates in Europe caused by PM2.5 according to (EEA, 2018)

Country Year Austria Belgium Bulgaria Croatia Cyprus Czechia Denmark Estonia

2011 (EEA, 2014) 6768 10304 10806 No data 710 10872 3979 647

2012 (EEA, 2015) 6100 9300 14100

2013 (EEA, 2016a) 6960 10050 13700

2014 (EEA, 2017) 5570 8340 13620

2015 (EEA, 2018) 5900 7400 14200

4500

4820

4430

4500

790 10400 2900 620

450 12030 2890 690

600 10810 3470 750

750 10100 2800 560

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Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxemburg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden United Kingdom Albania Andorra Bosnia and Herzegovina North Macedonia Iceland Kosovo under UNSCR 1244/99 Lichtenstein Monaco Montenegro Norway San Marino Serbia Switzerland EU 28 Total

2046 46339 69762 10700 15952 1229 65544 1789 2556 286 247 12634 42412 5707 28582 6300 1938 25046 4221

1900 43400 59500 11100 12800 1200 59500 1800 2300 250 200 10100 44600 5400 25500 5700 1700 25500 3700

1730 45120 73400 13730 12890 1520 66630 2080 3170 280 230 11530 48270 6070 25330 5620 1960 23940 3020

2150 34880 66060 11870 11970 1480 59630 2190 3350 230 220 11200 46020 5170 23960 5160 1710 23180 3710

1500 35800 62300 12000 12800 1100 60600 1600 2600 240 240 9800 44500 5500 25400 5200 1800 27900 3000

39450

37800

37930

37600

31300

2042 51

2200 60

2010 40

1670 40

1400 50

3412

3500

3620

3450

3700

1763

3000

3360

3060

3000

54

100

80

80

60

No data

No data

3530

3290

3700

16 29 482 1473 25 13063 4394 430219 458065

20 30 570 1700 30 13400 4300 403000 432000

20 20 600 1590 30 10730 4980 436000 467000

20 20 550 1560 30 10770 4240 399000 428000

20 20 640 1300 30 13000 4200 391000 422000

152 153

Supplementary material 2 presents the most recent direct measurements of PM2.5 and

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PM10 in the 50 cities with the highest concentrations (annual mean) across Europe. The list of

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cities that are most polluted by PM2.5 has been created using data from 1,234 European

156

cities. In terms of PM2.5, annual mean concentrations range from 26 to 65 µg/m3, and the

157

data were collected between 2016 and 2018. According to direct measurements of PM2.5

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concentration, the 18 most polluted cities are located in Poland; 12 are Italian cities; six are in

7

159

Turkey; five are in Czechia; Bosnia, Hercegovina and Bulgaria each have three; two are from

160

Croatia; and one city is located in North Macedonia. The list of cities that are most polluted

161

by PM10 has been created using measurements from 2,590 European cities. Annual mean

162

concentrations of PM10 range from 55 to 140 µg/m3, and data were collected between 2012

163

and 2016 (mostly from 2016). Analogically (as before), taking into account only direct

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measurements, it can be seen that one country is definitely in the lead position: 38 cities from

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the list are located in Turkey; five are from North Macedonia; two are located in Bosnia,

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Hercegovina and Bulgaria; and Belarus and Poland both have one city on this list.

167

Supplementary material 3 contains the most recent direct measurements of PM2.5 and

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PM10 in the 50 cities with the highest concentrations (annual mean) in context of the

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European Union countries. The list of cities that are most polluted by PM2.5 has been created

170

using measurements from 1,166 cities within European Union borders. Annual mean

171

concentrations of PM2.5 range from 25 to 41 µg/m3, and data were collected between 2016

172

and 2018 (mostly from 2018). Taking into account only direct measurements of PM2.5

173

concentrations, it can be seen that 23 cities are located in Poland; 14 are in Italy; seven are

174

located in Czechia; four are in Bulgaria; and two are in Croatia. The list of cities most

175

polluted by PM10 has been created using measurements from 2,335 cities in the European

176

Union. Annual mean concentrations of PM10 range from 41 to 61 µg/m3, and data were

177

collected between 2016 and 2018 (mostly from 2018). Analogically (as before), taking into

178

account only direct measurements, it can be seen that one country is definitely in the lead

179

position: 24 cities are located in Poland; 16 are in Bulgaria; three are in Greece; Italy and

180

Cyprus both have two; and France and Croatia both have one city on the list.

181

The air conditions in Poland are among the worst in the EU. According to the EEA’s

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2017 Air Quality in Europe report (EEA, 2017), in terms of PM2.5 and PM10, Poland has the

183

highest and second highest concentrations, respectively, in the EU. According to the 2018

8

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WHO Global Ambient Air Quality Database (WHO, n.d.), 36 of the 50 most polluted cities in

185

the EU are located in Poland. Bulgaria is characterized by the largest share of cities violating

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the EU’s 2020 air quality target (83 %), and Poland is the second worst (72 %). Across

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Europe, the worst situation is in Turkey where over 90 % of cities exceed the EU’s target.

188

Supplementary material 4 presents the most recent direct measurements of PM2.5 and PM10

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in cities with the highest concentrations (annual mean) in Poland. The list of cities that are

190

most polluted by PM2.5 has been created using data from 80 cities within Polish borders.

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Annual mean concentrations of PM2.5 range from 21 to 34 µg/m3. With the exception of two

192

records (Bielsko Biala and Zloty Potok), data were collected in 2018. The list of cities most

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polluted by PM10 has been created using data from 199 Polish cities. Annual mean

194

concentrations of PM10 range from 35 to 56 µg/m3, and data were collected between 2013

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and 2016 (mostly from 2016).

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2. MATERIALS AND METHODS

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2.1.STATISTICAL CONSIDERATIONS

199

For the purpose of creating this article, the Airly sp. z o.o. company has made available

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an archived database for 2017, covering the entire area of Krakow. From a total of 100

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sensors, five were selected, characterized by relatively full, uninterrupted data. The selected

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sensors represent the north, east, south, west and central areas of the city, providing an

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overview of the entire agglomeration. In the database, PM1, PM2.5 and PM10 concentration

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measurements are recorded every hour. However, in this study, the authors have focused on

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the 2.5 and 10 µm fractions. Arithmetic averages of PM2.5 and PM10 concentrations were

206

calculated monthly for each area of the city in order to identify seasonal variability and the

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influence of heating periods. Airly designed and created a network of sensors that measure

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real-time concentrations of particulate matter (PM1, PM2.5 and PM10) in relation to 9

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meteorological conditions (air temperature, pressure and humidity). For the selected sensors,

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basic statistical considerations were made in order to obtain daily and monthly average values

211

of PM2.5 and PM10 concentrations, as well as monthly average values for air temperature,

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humidity and atmospheric pressure. The highest annual PM2.5 and PM10 concentrations were

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recorded, as well as the number of days when concentrations exceeded the standard limits set

214

by EU legislation (EEA, 2016b).

215

The Shapiro Wilk test was used to diagnose the normality of data distribution, and a Tau

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Kendall correlation matrix was performed using Statistica 13.1 Software.

217

2.2.OBTAINING THE SENSOR MEASUREMENT VALUES

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Measurements were based on a beam of light reflected at the right angle. Dust

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absorption is continuously measured and used to calibrate the sensors. On the basis of the

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signal from the detector, special algorithms recognize both the size (assignment to one of the

221

categories) and number of particles at any given moment. In combination with air flow data,

222

this enables the final result to be given in µg/m3. The sensors send measurements every

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minute, firstly, converting the signal from the detector into a dust concentration and then

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continuously calculating the value in µ/m3 per minute. Device calibration was carried out after

225

approximately a year in accordance with the measurement stations of the Regional

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Inspectorate of Environmental Protection in Krakow. Calibration also complied with the

227

European Commission’s guide to equivalence studies (EC Working Group on Guidance for

228

the Demonstration of Equivalence, 2010). For comparative analysis of the results, Airly used

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the RIVM_PM_equivalence_v2.9.xls calculation sheet from the Dutch Institute of Health and

230

Environmental Protection (Rijksinstituut voor Volksgezondheid en Milieu). This sheet is

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recommended by the European Commission for comparing data. This is the first stage of

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calibration that devices must be subjected to. In the next step, they are calibrated to so-called

233

local conditions and undergo periodic calibration (after the smog season).

234

2.3.ANTI-SMOG HALF-MASK FILTER INVESTIGATION

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An anti-smog half-mask filter was used in this study as the medium for PM

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collectivization. The anti-smog half-mask was used in the Krakow agglomeration during the

237

winter season of 2018 (February to March). This time of year is characterized as the heating

238

season. Consequently, PM concentrations often reach high levels. It is supposed that the

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amount and character of the PM studied reflects the amount of pollution that would get into

240

the respiratory system of a person who does not use a mask. The experiment started at the

241

beginning of February 2018 and continued until the end of the product’s life (about 30 to 40

242

man-hours) at the end of March 2018. The filters were then separated from the anti-smog

243

mask, and the next stage involved preparing samples for scanning electron microscopy

244

analysis. In order to obtain a greater resolution, separated samples were subjected to coating

245

with a very thin layer of carbon (about 50 nm) using a Quorum Q150T turbo-pumped sputter

246

coater. A Quanta FEG 250 FEI scanning electron microscope (equipped with an EDAX

247

energy dispersive spectrometer (SEM-EDS)) was used to perform the morphological study,

248

and qualitative and semi-quantitative chemical analysis of the collected PM. Analysis was

249

performed in a high vacuum mode using 15.00 kV of HV. A secondary electron detector (SE)

250

was used to obtain images.

251

3. RESULTS

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3.1.THE PM ISSUE IN THE KRAKOW AGGLOMERATION

254

Every year, during the winter season, Krakow struggles with a smog like that of

255

London. This type of smog affects many regions and the situation in Krakow is, therefore,

11

256

typical of cities across the world. According to annual reports by the Polish Central Statistical

257

Office, in the Krakow agglomeration, average standardized percentile values (S90.4) for PM2.5

258

24-h concentrations in 2013 and 2014 were 99.2 µg/m3 and 108 µg/m3, respectively

259

(acceptable value: 50 µg/m3). Standardized average annual concentrations reached 51.1 µg/m3

260

and 53.1 µg/m3 (limit value: 40 µg/m3) (Central Statistical Office. CSO - Regional and

261

Environmental Surveys Department, 2015, 2014) In 2015, annual mean concentrations of

262

suspended PM2.5 and PM10 decreased to 36.9 µg/m3 (limit value: 25 µg/m3) and 54.9 µg/m3

263

(limit value: 40 µg/m3), respectively (Central Statistical Office. CSO - Regional and

264

Environmental Surveys Department, 2016). The following year (2016) brought another drop

265

in PM concentrations. Annual mean concentrations of suspended PM2.5 and PM10 decreased

266

to 32.4 µg/m3 and 43.7 µg/m3 (limit values remained at the same level and are still valid

267

today), respectively (Central Statistical Office. CSO - Regional and Environmental Surveys

268

Department, 2017). For some time, the government of the Lesser Poland Voivodeship and

269

city of Krakow have been trying to counteract air pollution by means of a wide array of legal

270

measures. A particularly important legal act is the resolution of the Council of the City of

271

Krakow from 5 November 2014 regarding adoption of an emission reduction programme for

272

the city of Krakow (Rada Miasta Krakowa, 2014). This resolution offers subsidies for

273

changing heating systems and installation of renewable energy sources, heat pumps and solar

274

collectors. The resolution of the Lesser Poland Council has continually sought to introduce

275

total prohibition of the use of solid fuels (Sejmik Województwa Małopolskiego, 2016).

276

Between 2013 and 2017, over 18,000 old-fashioned ovens were replaced in Krakow (27,000

277

overall in the Voivodeship). The cost of implementing investments related to the reduction of

278

surface emissions between 2013 and 2017 is estimated to be 1.2 billion PLN, including 357

279

million PLN for the decommissioning of old boilers and solid fuel stoves. Other expenses

280

include thermo-modernization of buildings (443 million PLN), installation of renewable

12

281

energy sources (219 million PLN), and expansion and modernization of heating networks

282

(190 million PLN) (Małopolska in a helthy atmosphere, n.d.). The next tool of particular

283

significance is implementation of the regional “LIFE” programme. The LIFE project,

284

coordinated by the Lesser Poland Voivodeship, engages a total of 62 partners, and its aim is to

285

accelerate implementation of measures to improve air quality, which were planned under the

286

Air Protection Programme for the Lesser Poland Voivodeship. The value of the project is

287

about 17 million euros, and EU funding amounts to about 60 % of this. The project was

288

implemented in October 2015 and is scheduled to run until the end of 2023. Unfortunately, at

289

the beginning of 2019, it was estimated that about 5,000 old-fashioned ovens/stoves remained,

290

especially in households. According to the above-mentioned legal measures, all heating

291

devices that do not comply with emission standards must be disassembled and replaced until

292

the end of September 2019. Another tool aiming to fight air pollution is standard PN-EN 303-

293

5:2012 (“PN-EN 303-5:2012,” 2012). This standard relates to solid fuel boilers with manual

294

and automatic fuel hoppers running on nominal power up to 500 kW. The standard sets the

295

terminology, requirements, testing, marking and emission requirements for these kinds of

296

boilers. It also introduced emissivity classes. Boilers in the fifth class have been allowed on

297

the market since July 2017 under Polish legislation.

298

3.2. AIRLY PLATFORM – STATISTICAL ANALYSIS

299

An important tool for raising public awareness about smog is Airly sp. z o.o. The

300

company mission is to monitor and inform millions of people about the current state of air

301

quality. The company launched about 3000 sensors, mostly located in Poland. The first

302

sensors were also launched in Germany (four in Berlin and three in Berghausen); the UK (two

303

in London and Windsor, one in Birmingham and one in Norwich); Ireland (one in Dublin);

304

France (one in Paris); Italy (one in Bulgarograsso); Greece (one in Saloniki); Austria (one in

305

Wien); Romania (15 in Bucharest and one in Ploiești); Spain (three in Madrid and one in 13

306

Barcelona); North Macedonia (three in Skopje, eight in Bitola, one in Gistiwar and one in

307

Tetovo); and Norway (Lorenskøg, near Oslo). In the Lesser Poland Voivodeship, there is a

308

network of 385 sensors (implemented in January 2019). In the city of Krakow, there is a

309

network of 100 Airly sensors, which allows accurate diagnosis of air conditions across the

310

city. Figure 1 and Table 2 below show PM2.5 and PM10 characteristics during 2017. These

311

monthly values are arithmetic averages based on a set of hourly measurements. Figure 1

312

clarifies that the measurements from sensor number 222 (from the west part of Krakow) are

313

characterized by the highest monthly PM2.5 and PM10 concentrations during the winter

314

season. The evident influence of the heating period is shown. The highest values of PM2.5

315

and PM10 concentrations were recorded between January and March, and October and

316

December 2017. This is the peak period for household heating in Poland and is one of the

317

reasons for low emissions and air pollution (due to the high popularity of coal and wood as

318

fuel).

14

319 320 321 322

Figure 1. Monthly averages of PM2.5 and PM10 from five Airly sensors representing north, east, south, west and central Krakow

323

the EU Air Quality Directive (2008/50/EC) (EEA, 2016b), the daily limit for PM10

324

concentrations (within an average hourly period) is 50 µg/m3. If average PM10 concentrations

325

exceed the 50 µg/m3 limit for more than 35 days a year, this is associated with a very poor

326

state of air quality. The five representative sensors shown in the Table 2 below indicate that,

Average PM concentrations significantly exceed environmental standards. According to

15

327

in the case of Krakow, the 50 µg/m3 limit for average PM10 levels has already been reached

328

by mid-February. Calculations state that these five sensors recorded the following days when

329

the daily PM10 limit was exceeded: sensor number 220 (north part of the city) – 116 days;

330

sensor number 210 (east part of the city) – 103 days; sensor number 202 (south part of the

331

city) – 134 days; sensor number 222 (west part of the city) – 106 days; sensor number 204

332

(central part of the city) – 107 days. Table 2 below also shows a set of information about

333

meteorological conditions influencing PM concentrations. The data show average monthly

334

air temperature and humidity (within average hourly periods) as well as a range of

335

atmospheric pressures. The data relating to maximum PM 2.5 and PM10 concentration values

336

are particularly worthy of mention. These data indicate the presence of incidental states with

337

extremely high PM concentration values. The highest PM2.5 and PM10 concentrations

338

measured were 353 µg/m3 and 539 µg/m3, respectively (recorded on 11 January 2017 at

339

11pm). Moreover, sensor number 220 noted 10 days with an hourly PM10 average higher

340

than 200 µg/m3, which consequently means the city should have been raising the alarm

341

(recommending that people limit their outdoor activity because the norm had been exceeded

342

four times over). Other sensors (numbers 210, 202, 222 and 204) noted the following number

343

of days when average PM10 concentrations exceeded 200 µg/m3: 6, 17, 17 and 5,

344

respectively. Sensor numbers 202 and 222 also measured hourly averages of PM10 higher

345

than 200 µg/m3 on two and four days, respectively. Furthermore, sensor numbers 202 (south),

346

222 (west) and 210 (east) noted hourly averages of PM10 higher than 300 µg/m3 on two, four

347

and one day(s), respectively.

348 349

Table 2. Meteorological conditions, maximum PM2.5 and PM10 values, and days when average PM10 concentrations exceeded 50 µg/m.

Months

I

II

III

IV

V

VI

VII

VIII

IX

North part of Kraków (sensor number 220 – Rogatka Street) Av. Temp. [°C] -2.1 0.6 5.3 6.7 13.7 17.9 18.1 18.8 12.5 Av. pressure [hPa] 1014 - 1021 Av. humidity [%] 38.9 71.1 72.8 74.9 77.9 64 70.5 72 83.6

X

XI

XII

9.1

4.4

1.8

84.1

85.3

79.7

16

CPM2.5 (max.) [µg/m3] CPM10 (max.) [µg/m3] Days with CPM10>50 Av. Temp. [°C] Av. pressure [hPa] Av. humidity [%] CPM2.5 (max.) [µg/m3] CPM10 (max.) [µg/m3] Days with CPM10>50

286 451 28

231 324 19

159 219 17

147 200 5

90 126 3

123 184 0

210 310 0

232 335 1

86 119 2

East part of Kraków (sensor number 210 – Sołtysowska Street) -1 1.3 6 7.3 14.5 18.5 18.1 19.8 12.9 1015 - 1024 35.6 69.8 71.9 74.8 77.9 64.4 72.4 72 84.9 269 219 142 95 62 32 34 42 51 429 306 196 125 82 55 56 67 78 23 18 17 5 2 0 0 1 2

87 123 10

185 254 19

163 223 12

9.3

4.2

1.6

85.8 75 107 8

88 132 180 18

84.3 107 150 9

3.4

1

77.5 168 227 21

67.7 169 229 13

4

1.5

84.4 183 245 18

79.5 173 231 13

4.4

2.2

88.7 161 219 18

85.4 129 178 11

South part of Kraków (sensor number 202 – Antoniego Szylinga Street) Av. Temp. [°C] -4.4 -0.2 4.6 6 13.1 17.8 17.4 18.5 11.4 8.4 Av. pressure [hPa] 1016 - 1021 Av. humidity [%] 62 79.3 71.7 74.9 77.9 60.6 67.3 63.7 82.2 78.7 3 313 273 216 100 84 55 32 49 63 105 CPM2.5 (max.) [µg/m ] 3 504 406 301 138 112 81 54 75 88 148 CPM10 (max.) [µg/m ] Days with CPM10>50 29 21 24 6 6 0 0 1 3 10 Av. Temp. [°C] Av. pressure [hPa] Av. humidity [%] CPM2.5 (max.) [µg/m3] CPM10 (max.) [µg/m3] Days with CPM10>50

West part of Kraków (sensor number 222 – Widłakowa Street) -1.7 1 5.9 7.1 14.3 18.3 18.5 19.4 12.9 9.2 1014 -1023 34.8 67.6 69.1 74.2 76.7 62.8 69 67.2 81.4 82.5 353 284 179 86 60 40 96 46 64 90 539 404 246 116 76 59 132 68 85 122 28 22 15 2 1 0 0 0 1 6

Central part of Kraków (sensor number 204 – Mikołajska Street) Av. Temp. [°C] -0.9 1.4 6.2 7.6 14.6 18.6 19 20.1 13.3 9.4 Av. pressure [hPa] 1015 - 1021 Av. humidity [%] 54.5 78.3 72.7 75.1 78.7 67.1 73.3 73.7 84.6 86.3 3 268 230 141 75 113 73 37 52 54 98 CPM2.5 (max.) [µg/m ] 419 323 197 106 158 101 59 76 80 136 CPM10 (max.) [µg/m3] Days with CPM10>50 23 19 16 4 2 0 0 1 2 11

350 351

To gain a deeper understanding and examine the interrelationships between

352

meteorological factors and PM concentrations, a correlation matrix was performed. The

353

following data were obtained: average monthly concentrations of PM10 (CPM10 Av. [µg/m3]);

354

the maximum value of PM10 concentrations in given months (CPM10 (max.) [µg/m3]); the

355

number of days when PM10 concentrations were above 50 µg/m3 (days with CPM10 > 50);

356

average monthly concentrations of PM2.5 (CP2.5 Av. [µg/m3]); the maximum value of PM2.5

357

concentrations in given months (CPM2.5 (max.) [µg/m3]); average monthly temperature (Av.

358

temp. [°C]); average monthly humidity (Av. humidity [%]); and average monthly atmospheric 17

359

pressure (Av. pressure [hPa]). To determine whether the data distribution was

360

normal/abnormal, the Shapiro Wilk test was performed. The results are presented in Table 3

361

and Figure 2 below.

362

Table. 3. Shapiro Wilk test for PM and meteorological parameters.

Sensor 220

Sensor 210

Sensor 202

Sensor 222

Sensor 204

N

W

W

W

W

W

W

W

p

W

p

CPM10 Av. [µg/m3]

12

0.783

0.786

0.786

0.762

0.809

0.809

0.762

0.004

0.786

0.007

CPM10 (max.) [µg/m3]

12

0.932

0.819

0.868

0.830

0.874

0.874

0.830

0.021

0.819

0.015

Days with CPM10>50

12

0.893

0.867

0.897

0.842

0.874

0.874

0.842

0.029

0.867

0.060

CP2.5 Av. [µg/m3]

12

0.794

0.796

0.799

0.778

0.829

0.829

0.778

0.005

0.796

0.008

CPM2.5 (max.) [µg/m3]

12

0.943

0.869

0.901

0.859

0.907

0.907

0.859

0.048

0.869

0.064

Av. Temp. [°C]

12

0.935

0.934

0.947

0.931

0.933

0.933

0.931

0.394

0.934

0.420

Av. humidity [%]

12

0.805

0.811

0.920

0.814

0.922

0.922

0.814

0.013

0.811

0.013

Av. pressure [hPa]

12

0.882

0.780

0.974

0.815

0.890

0.890

0.815

0.014

0.780

0.006

363 364

18

365 366

Figure 2.Matrix graphs of variables dispersion according to Table 3.

19

367 The test showed that some data were characterized by an abnormal distribution. As a

369

consequence, a non-parametric Tau Kendall correlation matrix was used. Table 4 below

370

presents the Tau Kendall correlation coefficients calculated. Various interesting relationships

371

were observed. There was a very strong positive correlation between CPM10 Av. and CP10 > 50,

372

indicating a strong relationship between average monthly concentrations of PM10 fraction

373

and the number of days when PM10 exceeded 50 µg/m3. A strong positive correlation was

374

also found between CPM10 Av. and CPM2.5 Av. This observation confirms correct and effective

375

operation of calibration algorithms for PM10 and PM2.5 concentrations (measurements were

376

taken from one sensor, but each fraction had a separate calibration). A strong negative

377

correlation was observed for the relation of CPM10 Av. and CPM2.5 Av. with Av. temp. This

378

indirectly indicates that as the average temperature decreases, particulate matter pollution

379

increases (mainly due to low emissions). A weak positive correlation was also observed in the

380

relation of CPM10 Av. and CPM2.5 Av. with Av. pressure. This can be explained by the

381

occurrence of high-pressure weather centres, which often carry a mass of cold air (in turn

382

causing an increase in low emissions). A moderate positive correlation was observed in the

383

relationship between CPM10 Av. and CPM2.5 Av., and between CPM10 max. and CPM2.5 max.,

384

respectively. This observation indicates the rather incidental and spontaneous nature of

385

occurrence of the highest PM10 and PM2.5 concentrations.

386

Table. 4. Tau Kendall correlation coefficients for PM and meteorological parameters.

North part of Kraków (sensor number 220 – Rogatka Street)

368

CPM10 CPM10 CPM10 Av. (max.) >50 1 0.394 0.954 0.394 1 0.339 0.954 0.339 1 0.970 0.364 0.923 0.394 1.000 0.339

CP2.5 CPM2.5 Av. (max.) 0.970 0.394 0.364 1.000 0.923 0.339 1 0.364 0.364 1

Av. Av. Av. Temp. humidity pressure -0.758 0.091 0.455 -0.273 -0.394 0.212 -0.800 0.123 0.400 -0.727 0.121 0.485 -0.273 -0.394 0.212

CPM10 Av. [µg/m3] CPM10 (max.) [µg/m3] Days with CPM10>50 CP2.5 Av. [µg/m3] CPM2.5 (max.) [µg/m3] Av. Temp. [°C] -0.758 -0.273 -0.800 -0.727 -0.273 1 Av. humidity [%] 0.091 -0.394 0.123 0.121 -0.394 -0.030 Av. pressure [hPa] 0.455 0.212 0.400 0.485 0.212 -0.273

-0.030 1 -0.212

-0.273 -0.212 1

20

West part of Kraków (sensor South part of Kraków (sensor East part of Kraków (sensor number 222 – Widłakowa number 202 – Antoniego number 210 – Sołtysowska Street) Szylinga Street) Street) Central part of Kraków (sensor number 204 – Mikołajska Street)

CPM10 Av. [µg/m3] CPM10 (max.) [µg/m3] Days with CPM10>50 CP2.5 Av. [µg/m3] CPM2.5 (max.) [µg/m3] Av. Temp. [°C] Av. humidity [%] Av. pressure [hPa]

CPM10 Av. [µg/m3] CPM10 (max.) [µg/m3] Days with CPM10>50 CP2.5 Av. [µg/m3] CPM2.5 (max.) [µg/m3] Av. Temp. [°C] Av. humidity [%] Av. pressure [hPa]

CPM10 Av. [µg/m3] CPM10 (max.) [µg/m3] Days with CPM10>50 CP2.5 Av. [µg/m3] CPM2.5 (max.) [µg/m3] Av. Temp. [°C] Av. humidity [%] Av. pressure [hPa]

CPM10 Av. [µg/m3] CPM10 (max.) [µg/m3] Days with CPM10>50 CP2.5 Av. [µg/m3] CPM2.5 (max.) [µg/m3] Av. Temp. [°C] Av. humidity [%] Av. pressure [hPa]

CPM10 Av. 1 0.909 0.946 0.970 0.909 -0.788 -0.030 0.394 CPM10 Av. 1 0.909 0.915 1.000 0.909 -0.788 0.091 0.273 CPM10 Av. 1 0.697 0.907 0.970 0.727 -0.879 0.061 0.424 CPM10 Av. 1 0.848 0.946 0.970 0.848 -0.848 0.182 0.273

CPM10 (max.) 0.909 1 0.915 0.879 1.000 -0.818 -0.061 0.364 CPM10 (max.) 0.909 1 0.884 0.909 1.000 -0.818 0.061 0.303 CPM10 (max.) 0.697 1 0.813 0.727 0.970 -0.697 0.000 0.303 CPM10 (max.) 0.848 1 0.853 0.879 1.000 -0.758 0.091 0.303

CPM10 >50 0.946 0.915 1 0.915 0.915 -0.822 0.016 0.326 CPM10 >50 0.915 0.884 1 0.915 0.884 -0.729 0.109 0.295 CPM10 >50 0.907 0.813 1 0.907 0.844 -0.875 0.094 0.406 CPM10 >50 0.946 0.853 1 0.915 0.853 -0.822 0.202 0.326

CP2.5 Av. 0.970 0.879 0.915 1 0.879 -0.758 0.000 0.364 CP2.5 Av. 1.000 0.909 0.915 1 0.909 -0.788 0.091 0.273 CP2.5 Av. 0.970 0.727 0.907 1 0.758 -0.848 0.091 0.455 CP2.5 Av. 0.970 0.879 0.915 1 0.879 -0.818 0.212 0.303

CPM2.5 (max.) 0.909 1.000 0.915 0.879 1 -0.818 -0.061 0.364 CPM2.5 (max.) 0.909 1.000 0.884 0.909 1 -0.818 0.061 0.303 CPM2.5 (max.) 0.727 0.970 0.844 0.758 1 -0.727 0.030 0.273 CPM2.5 (max.) 0.848 1.000 0.853 0.879 1 -0.758 0.091 0.303

Av. Temp. -0.788 -0.818 -0.822 -0.758 -0.818 1 0.000 -0.242 Av. Temp. -0.788 -0.818 -0.729 -0.788 -0.818 1 -0.121 -0.182 Av. Temp. -0.879 -0.697 -0.875 -0.848 -0.727 1 -0.061 -0.303 Av. Temp. -0.848 -0.758 -0.822 -0.818 -0.758 1 -0.091 -0.182

Av. humidity -0.030 -0.061 0.016 0.000 -0.061 0.000 1 -0.394 Av. humidity 0.091 0.061 0.109 0.091 0.061 -0.121 1 0.091 Av. humidity 0.061 0.000 0.094 0.091 0.030 -0.061 1 -0.273 Av. humidity 0.182 0.091 0.202 0.212 0.091 -0.091 1 -0.182

Av. pressure 0.394 0.364 0.326 0.364 0.364 -0.242 -0.394 1 Av. pressure 0.273 0.303 0.295 0.273 0.303 -0.182 0.091 1 Av. pressure 0.424 0.303 0.406 0.455 0.273 -0.303 -0.273 1 Av. pressure 0.273 0.303 0.326 0.303 0.303 -0.182 -0.182 1

387 388

According to the data of the Regional Inspectorate for Environmental Protection, the

389

activities carried out so far have helped facilitate a reduction in PM10 of over 900 tons (over

390

720 tons in the case of PM2.5) (Małopolska in a helthy atmosphere, n.d.).

391 392

3.3. ANTI-SMOG

HALF-MASK

FILTER

SCANNING

ELECTRON

MICROSCOPY INVESTIGATION

21

393

Figures 3 and 4 below show microphotographs of eight PM objects, observed on the

394

mask filters, together with qualitative and semi-quantitative analysis of their chemical

395

composition. The black spot is marked as the place where the EDS point analysis was made.

396

To the right of the SEM image, the EDS spectra (with the approximate chemical composition

397

calculated) are presented. In terms of chemistry, the dominant elements comprising the

398

studied particles include aluminium, silicon, calcium, sodium, magnesium, iron, potassium,

399

sulphur, chlorine and titanium. Carbon is likely to be a highly important constituent of the

400

chemical composition of the observed dusts. However, it is difficult to estimate its exact

401

content in this case due to the process of sample coating with a carbon layer (for example,

402

insufficient research material was collected to enable use of IR to examine the carbon

403

content). This method significantly improves electric discharge from the sample in order to

404

achieve higher magnification (the main aim of this study being to analyse the morphology and

405

size of PM objects). The objects observed have different shapes. Object 1 is characterized by

406

a longitudinal spindle shape. Object 2 has a spherical form, and the remaining objects (3 to 8)

407

possess irregular shapes. The entire set of analysed objects may infiltrate the human body

408

through the respiratory system, which poses a serious health risk (Li and Gao, 2014; Lin et al.,

409

2018; Wan et al., 2015; Wu et al., 2018).

22

410 411 412

Figure 3. SEM microphotographs of PM objects (1 to 4) with a qualitative, semi-quantitative chemical composition

23

413 414 415

Figure 4. SEM microphotographs of PM objects (5 to 8) with a qualitative, semi-quantitative chemical composition

416

Using an additional SEM tool designed to examine the length and size of objects, the

417

presence of a whole spectrum of PM fractions was revealed (Fig. 5). The shape of 1 and 2

418

object is sphere-like, and their diameter oscillates at approximately 0.6 µm. Their origin may

419

be related to household heating through coal combustion (with a shape very similar to fly ash

420

particles). The next four objects (3 to 6) are characterized by a more irregular shape (shapes 3

421

and 4 are similar to the sphere). Their diameters range from approximately 1 to 2 µm, and 24

422

they are typical of PM2.5. Samples 7 to 10 are classified as PM10. Their diameters range

423

from 4 to approximately 10 µm. They possess different shapes – irregular and spherical. It can

424

be assumed with a high probability that sample 7 is fly ash. The last two samples (11 and 12)

425

possess diameters above 10 µm and are characterized by a shape with sharper edges.

25

426 427

Figure 5. SEM microphotographs of size analysis of PM objects

26

428 429

4. CONCLUSIONS

430

Air pollution remains one of the most significant global issues with serious health

431

consequences. Various initiatives at local, national and international levels deserve praise and

432

should continue in order to improve air quality conditions.

433

In Poland, annual mean concentrations of PM2.5 range from 21 to 34 µg/m3, while PM10

434

values range from 35 to 56 µg/m3. Due to the considerable popularity of coal and wood (the

435

main fuels used for heating households), public awareness of the link between poor-quality

436

boilers/stoves and air quality remains low (especially in rural areas), and Poland still faces the

437

problem of poor air quality.

438

Krakow is one of the Polish cities that has a notorious problem with smog (especially in

439

winter, the heating period). Krakow is also one of the cities leading the fight against poor air

440

quality. A number of actions and initiatives to improve air quality are noteworthy:

441

- the resolution of the Council of the City of Krakow dated 5 November 2014

442

regarding adoption of an emission reduction programme in the city of Krakow;

443

- the resolution of the Lesser Poland Council, introducing total prohibition of the use

444

of solid fuels;

445

- the work of Airly sp. z o.o.;

446

- the LIFE project;

447

- standard PN-EN 303-5:2012.

448

Statistical analysis of PM2.5 and PM10 (as well as meteorological conditions across

449

Krakow) was performed using the 2017 archived database obtained from Airly. Due to their

450

localization (in north, east, south, west and central parts of the city), five selected sensors

451

reflect the situation across the city. The greatest risk associated with exposure to high

27

452

concentrations of PM was found to occur in Krakow during the periods January to March and

453

October to December. These periods are characterized as the peak of the heating season. The

454

highest recorded value for PM2.5 was found to be 353 µg/m3, and the highest for PM10

455

equalled 539 µg/m3. Each of the five sensors indicated that the number of days when the daily

456

PM10 average exceeded 50 µg/m3 was over 100 (more than 35 indicates very poor air

457

quality). The number of days when the daily PM10 average exceeded 200 µg/m3 ranged from

458

a few to over a dozen.

459

Use of statistical tools including the Shapiro Wilk test and Tau Kendall correlation

460

matrix turned out to be a useful procedure to help understand the relationships between PM

461

concentrations and meteorological conditions. The strongest correlations were observed

462

between average monthly concentrations of PM10 and the number of days when PM10

463

concentrations exceeded 50 µg/m3, average monthly concentrations of PM2.5 and average

464

temperatures. A weak or moderately weak correlation was observed for relationships between

465

average monthly PM10 concentrations, average atmospheric pressure and maximum values of

466

PM10 and PM2.5.

467

Considering the above-mentioned facts as well as results of the scanning electron

468

microscopy investigation of the anti-smog half-mask filter, it may be concluded that in cities

469

like Krakow, use of anti-smog masks is highly recommended during periods of increased

470

household heating.

471

A number of similar platforms are also operating elsewhere in the world, considering

472

and monitoring air quality in PM2.5 and PM10 particulate concentrations (“Beijing Air

473

Pollution: Real-time Air Quality Index (AQI),” n.d., “HackAir,” n.d., “The AirCasting

474

Platform,” n.d., “World’s Air Pollution: Real-time Air Quality Index,” n.d.) but none of these

475

seem to be growing as dynamically or providing data from areas as large as Airly. This fact

28

476

makes the selected platform a very helpful tool for conducting research into the characteristics

477

of the presence of particulate matter in the air.

478

As well as promoting positive behaviours and initiatives to improve air quality, the

479

information contained in this article can significantly contribute to increasing societal

480

awareness of PM and the implications for human health and quality of life. In order to achieve

481

healthy air-quality conditions, further action is required on a local, national and international

482

scale. Future plans that include experiments using electromagnetic pumps as a tool will

483

facilitate collection of greater amounts of PM year by year. This approach will allow more

484

comprehensive study of the PM issue and performance of analyses on both a seasonal and

485

annual scale.

486

ACKNOWLEDGEMENTS

487

This work was financed within frame of statutory work of Mineral and Energy Economy

488

Research Institute Polish Academy of Sciences, Kraków.

489

The database for statistical study has been delivered from Airly sp. z o.o.

490 491

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32

Table 1. Annual premature death rates in Europe caused by PM2.5 according to (EEA 2018)

Country Year Austria Belgium Bulgaria Croatia Cyprus Czechia Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxemburg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden United Kingdom Albania Andorra Bosnia and Herzegovina North Macedonia Iceland Kosovo under UNSCR 1244/99 Lichtenstein Monaco Montenegro Norway San Marino Serbia Switzerland EU 28 Total

2011 (EEA 2014) 6768 10304 10806 No data 710 10872 3979 647 2046 46339 69762 10700 15952 1229 65544 1789 2556 286 247 12634 42412 5707 28582 6300 1938 25046 4221

2012 (EEA 2015) 6100 9300 14100

2013 (EEA 2016b) 6960 10050 13700

2014 (EEA 2017) 5570 8340 13620

2015 (EEA 2018) 5900 7400 14200

4500

4820

4430

4500

790 10400 2900 620 1900 43400 59500 11100 12800 1200 59500 1800 2300 250 200 10100 44600 5400 25500 5700 1700 25500 3700

450 12030 2890 690 1730 45120 73400 13730 12890 1520 66630 2080 3170 280 230 11530 48270 6070 25330 5620 1960 23940 3020

600 10810 3470 750 2150 34880 66060 11870 11970 1480 59630 2190 3350 230 220 11200 46020 5170 23960 5160 1710 23180 3710

750 10100 2800 560 1500 35800 62300 12000 12800 1100 60600 1600 2600 240 240 9800 44500 5500 25400 5200 1800 27900 3000

39450

37800

37930

37600

31300

2042 51

2200 60

2010 40

1670 40

1400 50

3412

3500

3620

3450

3700

1763

3000

3360

3060

3000

54

100

80

80

60

No data

No data

3530

3290

3700

16 29 482 1473 25 13063 4394 430219 458065

20 30 570 1700 30 13400 4300 403000 432000

20 20 600 1590 30 10730 4980 436000 467000

20 20 550 1560 30 10770 4240 399000 428000

20 20 640 1300 30 13000 4200 391000 422000

Table 2. Weather conditions, maximum PM2.5 and PM10 values, and days when average PM10 concentrations exceeded 50

µg/m. Months

I

II

III

IV

V

VI

VII

VIII

IX

Av. Temp. [°C] Av. pressure [hPa] Av. humidity [%] CPM2.5 (max.) [µg/m3] CPM10 (max.) [µg/m3] Days with CPM10>50

North part of Kraków (sensor number 220 – Rogatka Street) -2.1 0.6 5.3 6.7 13.7 17.9 18.1 18.8 12.5 1014 - 1021 38.9 71.1 72.8 74.9 77.9 64 70.5 72 83.6 286 231 159 147 90 123 210 232 86 451 324 219 200 126 184 310 335 119 28 19 17 5 3 0 0 1 2

Av. Temp. [°C] Av. pressure [hPa] Av. humidity [%] CPM2.5 (max.) [µg/m3] CPM10 (max.) [µg/m3] Days with CPM10>50

East part of Kraków (sensor number 210 – Sołtysowska Street) -1 1.3 6 7.3 14.5 18.5 18.1 19.8 12.9 1015 - 1024 35.6 69.8 71.9 74.8 77.9 64.4 72.4 72 84.9 269 219 142 95 62 32 34 42 51 429 306 196 125 82 55 56 67 78 23 18 17 5 2 0 0 1 2

X

XI

XII

9.1

4.4

1.8

84.1 87 123 10

85.3 185 254 19

79.7 163 223 12

9.3

4.2

1.6

85.8 75 107 8

88 132 180 18

84.3 107 150 9

3.4

1

77.5 168 227 21

67.7 169 229 13

4

1.5

84.4 183 245 18

79.5 173 231 13

4.4

2.2

88.7 161 219 18

85.4 129 178 11

South part of Kraków (sensor number 202 – Antoniego Szylinga Street) Av. Temp. [°C] -4.4 -0.2 4.6 6 13.1 17.8 17.4 18.5 11.4 8.4 Av. pressure [hPa] 1016 - 1021 Av. humidity [%] 62 79.3 71.7 74.9 77.9 60.6 67.3 63.7 82.2 78.7 313 273 216 100 84 55 32 49 63 105 CPM2.5 (max.) [µg/m3] 3 504 406 301 138 112 81 54 75 88 148 CPM10 (max.) [µg/m ] Days with CPM10>50 29 21 24 6 6 0 0 1 3 10 Av. Temp. [°C] Av. pressure [hPa] Av. humidity [%] CPM2.5 (max.) [µg/m3] CPM10 (max.) [µg/m3] Days with CPM10>50

West part of Kraków (sensor number 222 – Widłakowa Street) -1.7 1 5.9 7.1 14.3 18.3 18.5 19.4 12.9 9.2 1014 -1023 34.8 67.6 69.1 74.2 76.7 62.8 69 67.2 81.4 82.5 353 284 179 86 60 40 96 46 64 90 539 404 246 116 76 59 132 68 85 122 28 22 15 2 1 0 0 0 1 6

Central part of Kraków (sensor number 204 – Mikołajska Street) Av. Temp. [°C] -0.9 1.4 6.2 7.6 14.6 18.6 19 20.1 13.3 9.4 Av. pressure [hPa] 1015 - 1021 Av. humidity [%] 54.5 78.3 72.7 75.1 78.7 67.1 73.3 73.7 84.6 86.3 268 230 141 75 113 73 37 52 54 98 CPM2.5 (max.) [µg/m3] 3 419 323 197 106 158 101 59 76 80 136 CPM10 (max.) [µg/m ] Days with CPM10>50 23 19 16 4 2 0 0 1 2 11

Table. 3. Shapiro Wilk test for PM and meteorological parameters.

Sensor 220

Sensor 210

Sensor 202

Sensor 222

Sensor 204

N

W

W

W

W

W

W

W

p

W

p

CPM10 Av. [µg/m3]

12

0.783

0.786

0.786

0.762

0.809

0.809

0.762

0.004

0.786

0.007

CPM10 (max.) [µg/m3]

12

0.932

0.819

0.868

0.830

0.874

0.874

0.830

0.021

0.819

0.015

Days with CPM10>50

12

0.893

0.867

0.897

0.842

0.874

0.874

0.842

0.029

0.867

0.060

CP2.5 Av. [µg/m3]

12

0.794

0.796

0.799

0.778

0.829

0.829

0.778

0.005

0.796

0.008

CPM2.5 (max.) [µg/m3]

12

0.943

0.869

0.901

0.859

0.907

0.907

0.859

0.048

0.869

0.064

Av. Temp. [°C]

12

0.935

0.934

0.947

0.931

0.933

0.933

0.931

0.394

0.934

0.420

Av. humidity [%]

12

0.805

0.811

0.920

0.814

0.922

0.922

0.814

0.013

0.811

0.013

Av. pressure [hPa]

12

0.882

0.780

0.974

0.815

0.890

0.890

0.815

0.014

0.780

0.006

Central part of Kraków (sensor number 204 – Mikołajska Street)

West part of Kraków (sensor South part of Kraków (sensor East part of Kraków (sensor North part of Kraków (sensor number 222 – Widłakowa number 202 – Antoniego number 210 – Sołtysowska number 220 – Rogatka Street) Street) Szylinga Street) Street)

Table. 4. Tau Kendall correlation coefficients for PM and meteorological parameters.

CPM10 CPM10 CPM10 Av. (max.) >50 1 0.394 0.954 0.394 1 0.339 0.954 0.339 1 0.970 0.364 0.923 0.394 1.000 0.339

CPM10 Av. [µg/m3] CPM10 (max.) [µg/m3] Days with CPM10>50 CP2.5 Av. [µg/m3] CPM2.5 (max.) [µg/m3] Av. Temp. [°C] -0.758 -0.273 Av. humidity [%] 0.091 -0.394 Av. pressure [hPa] 0.455 0.212 CPM10 CPM10 Av. (max.) CPM10 Av. [µg/m3] 1 0.909 CPM10 (max.) [µg/m3] 0.909 1 Days with CPM10>50 0.946 0.915 CP2.5 Av. [µg/m3] 0.970 0.879 CPM2.5 (max.) [µg/m3] 0.909 1.000 Av. Temp. [°C] -0.788 -0.818 Av. humidity [%] -0.030 -0.061 Av. pressure [hPa] 0.394 0.364 CPM10 CPM10 Av. (max.) CPM10 Av. [µg/m3] 1 0.909 CPM10 (max.) [µg/m3] 0.909 1 Days with CPM10>50 0.915 0.884 CP2.5 Av. [µg/m3] 1.000 0.909 CPM2.5 (max.) [µg/m3] 0.909 1.000 Av. Temp. [°C] -0.788 -0.818 Av. humidity [%] 0.091 0.061 Av. pressure [hPa] 0.273 0.303 CPM10 CPM10 Av. (max.) CPM10 Av. [µg/m3] 1 0.697 CPM10 (max.) [µg/m3] 0.697 1 Days with CPM10>50 0.907 0.813 CP2.5 Av. [µg/m3] 0.970 0.727 CPM2.5 (max.) [µg/m3] 0.727 0.970 Av. Temp. [°C] -0.879 -0.697 Av. humidity [%] 0.061 0.000 Av. pressure [hPa] 0.424 0.303 CPM10 CPM10 Av. (max.) CPM10 Av. [µg/m3] 1 0.848 CPM10 (max.) [µg/m3] 0.848 1 Days with CPM10>50 0.946 0.853 CP2.5 Av. [µg/m3] 0.970 0.879 CPM2.5 (max.) [µg/m3] 0.848 1.000 Av. Temp. [°C] -0.848 -0.758 Av. humidity [%] 0.182 0.091 Av. pressure [hPa] 0.273 0.303

-0.800 0.123 0.400 CPM10 >50 0.946 0.915 1 0.915 0.915 -0.822 0.016 0.326 CPM10 >50 0.915 0.884 1 0.915 0.884 -0.729 0.109 0.295 CPM10 >50 0.907 0.813 1 0.907 0.844 -0.875 0.094 0.406 CPM10 >50 0.946 0.853 1 0.915 0.853 -0.822 0.202 0.326

CP2.5 CPM2.5 Av. (max.) 0.970 0.394 0.364 1.000 0.923 0.339 1 0.364 0.364 1 -0.727 0.121 0.485 CP2.5 Av. 0.970 0.879 0.915 1 0.879 -0.758 0.000 0.364 CP2.5 Av. 1.000 0.909 0.915 1 0.909 -0.788 0.091 0.273 CP2.5 Av. 0.970 0.727 0.907 1 0.758 -0.848 0.091 0.455 CP2.5 Av. 0.970 0.879 0.915 1 0.879 -0.818 0.212 0.303

-0.273 -0.394 0.212 CPM2.5 (max.) 0.909 1.000 0.915 0.879 1 -0.818 -0.061 0.364 CPM2.5 (max.) 0.909 1.000 0.884 0.909 1 -0.818 0.061 0.303 CPM2.5 (max.) 0.727 0.970 0.844 0.758 1 -0.727 0.030 0.273 CPM2.5 (max.) 0.848 1.000 0.853 0.879 1 -0.758 0.091 0.303

Av. Av. Av. Temp. humidity pressure -0.758 0.091 0.455 -0.273 -0.394 0.212 -0.800 0.123 0.400 -0.727 0.121 0.485 -0.273 -0.394 0.212 1 -0.030 -0.273 Av. Temp. -0.788 -0.818 -0.822 -0.758 -0.818 1 0.000 -0.242 Av. Temp. -0.788 -0.818 -0.729 -0.788 -0.818 1 -0.121 -0.182 Av. Temp. -0.879 -0.697 -0.875 -0.848 -0.727 1 -0.061 -0.303 Av. Temp. -0.848 -0.758 -0.822 -0.818 -0.758 1 -0.091 -0.182

-0.030 1 -0.212 Av. humidity -0.030 -0.061 0.016 0.000 -0.061 0.000 1 -0.394 Av. humidity 0.091 0.061 0.109 0.091 0.061 -0.121 1 0.091 Av. humidity 0.061 0.000 0.094 0.091 0.030 -0.061 1 -0.273 Av. humidity 0.182 0.091 0.202 0.212 0.091 -0.091 1 -0.182

-0.273 -0.212 1 Av. pressure 0.394 0.364 0.326 0.364 0.364 -0.242 -0.394 1 Av. pressure 0.273 0.303 0.295 0.273 0.303 -0.182 0.091 1 Av. pressure 0.424 0.303 0.406 0.455 0.273 -0.303 -0.273 1 Av. pressure 0.273 0.303 0.326 0.303 0.303 -0.182 -0.182 1

Highest urban annual mean of PM2.5 was recorded by WHO in Kanpur, India – 173 µg/m3 Highest urban annual mean of PM10 was recorded by WHO in Peshawar, Pakistan – 540 µg/m3 Tuzla and Tetovo are recently most PM polluted cities in Europe 24 of 50 most PM polluted cities in European Union are located in Poland Airly platform is an excellent PM pollution monitoring tool

Author Statement: Piotr Kunecki: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Writing - Original Draft, Writing - Review & Editing, Visualization Wojciech Franus: Supervision Magdalena Wdowin: Supervision, Writing - Review & Editing, Project administration, Funding acquisition

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: