Introduction

Air pollution is a significant threat to modern society, and it has been shown that it negatively affects both, the people’s health (Ghorani-Azam et al., 2016; Lave & Seskin, 2013; Shahriyari et al., 2022) and the environment (Saurabh Sonwani & Vandana Maurya, 2019; Stevens et al., 2020). Particulate matter (PM) with an effective aerodynamic diameter smaller than 2.5 and 10 μm (PM2.5 and PM10) have gained specific attention among air pollutants, and their effects on human health and ecosystems have become important research topics in recent years (Bodor et al., 2022; Faraji Ghasemi et al., 2020; Leão et al., 2022; Yunesian et al., 2019; Zoran et al., 2020).

High levels of PM10 have been associated with increased hospital admissions for lung and heart disease. In contrast, PM2.5 has a greater negative impact on human health than PM10 because it penetrates deeper into the respiratory system. Prediction of atmospheric composition aids significantly in air quality management; however, predicting air quality remains a challenge due to the processes’ complexity and the strong coupling across many parameters, which affects modeling performance (Biancofiore et al., 2017). Overall, the composition and concentration of all components in PM of all size fractions vary. This variability is most likely caused by natural and environmental factors such as human or natural sources, temperature and seasonal changes, and geographical location (Polichetti et al., 2009).

In Croatia, the National Network for Continuous Air Quality Monitoring uses ground stations to monitor atmospheric concentrations of PM2.5 and PM10 throughout the country. However, as noted by Li et al. (2020), ground station measurements are only applicable in local areas and cannot provide a broad perspective. Recently, several remote sensing-based methods have been developed for this issue. Estimating ambient PM2.5 and PM10 concentrations using observations from remote sensing satellites have been the subject of various studies to date (Lin et al., 2015; Zhang & Li, 2015; Chen et al., 2018a, b; Sun et al., 2019; Li et al., 2021). Scientists have also used high-resolution remote sensing imagery from Unmanned Aerial Vehicles (UAV) for computer simulation and comparison with real measurements (Cichowicz & Dobrzański, 2022), 3D investigation of air quality (Samad et al., 2022), pollutants detection (De Fazio et al., 2022), etc. However, the mentioned studies have been conducted over small study areas. For vast areas, the use of Sentinel-5P mission TROPOMI data to estimate PM2.5 and PM10 concentrations has been implemented by a few studies lately (Ahmed et al., 2022; Han et al., 2022; Li et al., 2022; Son et al., 2022; Wang et al., 2021). Sentinel-5P TROPOMI was launched on October 13, 2017, as the first Copernicus mission with the main objective of monitoring air pollutants in the atmosphere. It is the most recent global satellite mission in monitoring air quality and daily measures concentrations of ozone (O3), methane (CH4), formaldehyde (HCHO), carbon monoxide (CO), nitrogen oxide (NO2), sulphur dioxide (SO2), and aerosol—provided as an aerosol index (AI).

Wang et al. (2021) used Sentinel-5P and GEOS Forward Processing datasets (GEOS-FP) to develop a new approach for the daily estimation of full-coverage 5 km (0.05°) ambient concentrations of PM2.5 and PM10 over China. The estimation function is obtained by fusing the multisource (Sentinel-5P TROPOMI, GEOS Forward Processing, and ground-based stations) data via ensemble machine learning methods, such as the light gradient boosting machine. On the other hand, Han et al. (2022) did an interpolation-based fusion of Sentinel-5P TROPOMI, elevation, and regulatory grade ground station data for producing spatially continuous maps of PM2.5 concentrations over Thailand using different machine learning algorithms and comparing their accuracy. Furthermore, in their study, Son et al. (2022) proposed a deep learning-based surface PM2.5 estimation method using the attentive interpretable tabular learning neural network (TabNet) with five Sentinel-5P TROPOMI products (NO2, SO2, O3, CO, HCHO) over Thailand. They have tested the capability to estimate PM2.5 without aerosol optical property, which was used more traditionally. Moreover, they highlighted CO as the most influential chemical component and related it to the seasonal burning in southeast Asia. In contrast to traditional studies, Li et al. (2022) proposed a knowledge-informed neural network model for joint estimation of PM2.5 and O3 over China, in which satellite observations, reanalysis data, and ground station measurements are used. Their conclusion was that the joint estimation model achieves performance comparable to that of the separate estimation model but with higher efficiency. Ahmed et al. (2022) developed a convolutional neural network (CNN) model which uses Sentinel-5P TROPOMI data of seven different pollutants (AI, CH4, CO, HCHO, NO2, O3, SO2), as auxiliary variables to estimate daily average concentrations of PM2.5 in various cities in Pakistan from May 2019 to April 2020.

This research follows a new approach developed by Mamić et al. (2022) for accurately estimating atmospheric concentrations of PM2.5 and PM10 using Sentinel-5P and other open-source remote sensing data from the Google Earth Engine (GEE) platform, a geospatial processing platform created to store and analyze enormous data sets for analysis and decision making. As noted by Gorelick et al. (2017), GEE’s data catalog contains a repository of publicly available geospatial datasets, including observations from various satellite and aerial imaging systems in optical and non-optical wavelengths, environmental variables, weather and climate forecasts and hindcasts, land cover, topographic, and socioeconomic datasets. All of this data is pre-processed into a ready-to-use, but the information-preserving format allows efficient access and removes numerous obstacles associated with data management. In this study, machine learning methods have been used to create models that can effectively determine air quality. Machine learning approaches have been recognized as useful and accurate in developing spatially explicit models. Random forest was employed in this study, as one of the most commonly used algorithms when it comes to estimating PM2.5 and PM10 at both, national (Chen et al., 2018a, b; Stafoggia et al., 2019; Shao et al., 2020; Zhao et al., 2020) and regional (Huang et al., 2018; Yang et al., 2020) level.

Furthermore, several studies (Cichowicz et al., 2017; Xiao et al., 2015) have shown significant differences in the dispersion of atmospheric air pollution between heating and non-heating seasons. Accordingly, the main purpose of this study is to develop models that can be efficiently used to estimate the PM2.5 and PM10 in the Republic of Croatia for non-heating (June, July, and August 2021) and heating (December 2021, January, and February 2022) season at a national and regional scale. The motivation to develop regional-scale models lies in the variability mentioned above of PM. Therefore, regional models will try to tackle the questions of composition and concentration of PM2.5 and PM10 between different climatic and geographical features. The performance of developed models is evaluated using the metrics of the mean absolute error (MAE), the root mean squared error (RMSE), and the Pearson correlation coefficient (r). In addition, in situ and estimated PM2.5 seasonal values were compared to those available by Copernicus Atmosphere Monitoring Service (CAMS) on the national level.

The research objectives can be divided into two main sections: (i) developing PM2.5 and PM10 models for non-heating and heating season on a national scale and (ii) developing PM2.5 and PM10 models for non-heating and heating season on a regional scale.

The structure of this paper is as follows. The second section introduces the study area, describes the used materials, and provides an overview of the methodology. The third section reveals the results of this research, followed by a discussion. In the final section, conclusions are provided.

Materials and methods

Study area

The Republic of Croatia has been chosen as the study area for this research (Fig. 1). Concerns about air pollution in Croatia increased rapidly last year after the Institute for Health Metrics and Evaluation (IHME) announced that Croatia ranks fifth in the European Union (EU) in terms of deaths caused by polluted air, putting the country at the bottom of the EU in terms of air quality (Index, 2021).

Fig. 1
figure 1

Map of the Republic of Croatia with biogeographical regions and ground stations of the Croatian National Network for Continuous Air Quality Monitoring

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The Republic of Croatia is a country in Southeast Europe, covering 56,594 km2, and its capital is Zagreb. Croatia is primarily a lowland country. The lowlands (terrain below 200 m absolute altitude) represent 53.4% of Croatia’s territory, the hills (200 to 500 m absolute altitude) account for 25.6%, and the mountains and mountainous areas (above 500 m absolute altitude) account for 21.0%. The horseshoe shape reflects the importance of the continental and coastal regions, which are primarily linked by the karst mountain region (Hrvatska enciklopedija, 2021).

Biogeographical boundaries were gathered from the Emerald Network countries and EU member states. These were combined to create a map of biogeographical areas independent of political borders and cover all of Europe. The layer of biogeographical regions for all of Europe is available through the European Environment Agency (EEA, 2016). Accordingly, Croatia has three different biogeographical regions: Alpine, Continental, and Mediterranean.

The Alpine region of Croatia has a relatively cold and harsh climate, high altitudes, and a complex topography of the Dinaric Alps. With less than 10,000 residents, Ogulin and Gospić are the two largest towns in this region, the least inhabited in all of Croatia.

The Continental region of Croatia has a relatively flat landscape and a climate of strong contrasts between cold winters and hot summers. The Pannonian plain mainly influences it. The most populated cities in this region are Zagreb, Osijek, Slavonski Brod, Karlovac, and Varaždin.

The climate of Croatia’s Mediterranean region is known for having hot, dry summers and humid, and chilly winters. However, it can also be notoriously unpredictable, with sudden rainstorms or bursts of strong winds occurring at different times of the year. The Adriatic Sea greatly influences it. Split, Rijeka, Zadar, and Pula are the largest cities in this region.

Materials

Since the composition of PM2.5 and PM10 varies on multiple geographical and meteorological factors, we use multiple remote sensing data freely available on the GEE platform in this study. On the other hand, in situ data from the Croatian National Network for Continuous Air Quality monitoring stations will be used for validation as ground truth data.

In situ data

The ground stations in this study measure PM2.5 and PM10 automatically every hour in μg/m3 units. The collected data are freely available on the official website of the Croatian National Network for Continuous Air Quality Monitoring (ISZZ, 2022). First, the PM2.5 and PM10 raw hourly data were downloaded for all available stations for non-heating (June, July, and August 2021) and heating (December 2021 and January and February 2022) seasons in Croatia.

In Fig. 1, it is noticeable that the ground air quality monitoring stations are quite uniformly distributed at the national level. However, a closer look into their regional spatial distribution reveals an insufficient number of ground stations in the Alpine region, where data from just two stations were available for modeling, the non-heating season. On the other hand, only one station is available to model the Alpine region’s heating season. That being said, the lack of stations would significantly disrupt the stability and the validity of the models; therefore, their creation for the Alpine region was abandoned. Regarding the Continental region, ground stations are evenly distributed throughout the region, and data from eight stations were used for PM2.5 estimation in the non-heating season and from 11 for PM10. In the heating season, nine stations were available for PM2.5 and 14 for PM10. The irregular shape of the Croatian Mediterranean region makes it geographically challenging for modeling. In addition, there is a lack of ground stations in the central and southern parts of the region, especially on islands.

Nonetheless, data from 10 stations were available for PM2.5 estimation in both seasons. For PM10, 16 stations were available in the non-heating and 17 in the heating season. Despite everything, it was decided to develop PM2.5 and PM10 models for the Mediterranean region. On the national scale, due to some missing or invalid in situ data in the observed time range, data from 20 ground stations were used to estimate PM2.5 for both seasons, and for PM10, 30 stations were used for non-heating and 32 for the heating season.

Remote sensing data

The main data used to estimate this research’s PM2.5 and PM10 values is pre-processed L3 Sentinel-5P TROPOMI data of AI, CO, HCHO, NO2, O3, and SO2. On the other hand, meteorological data used in this study is from the National Oceanic and Atmospheric Administration (NOAA) which provides a dataset consisting of selected model outputs as gridded forecast variables through its Global Forecast System (GFS), which is updated four times daily (every 6 h). GFS data used are land surface temperature 2 m above the ground in °C (LST), specific humidity 2 m above ground in kg/kg (kilogram of water per kilogram of air) (HUM), and U and V component of wind 10 m above ground in m/s (U-WIND and V-WIND). Geographical data on elevation (DEM) used in this study is from NASA’s Shuttle Radar Topography Mission (SRTM), and slope data was derived from it. Moreover, soil pH data at ground level was retrieved from the map made by Hengl in 2018 available on GEE. Besides, information on all used datasets from GEE is given in Table 1.

Table 1 Remote sensing data from GEE used in this study (Mamić et al., 2022)
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Methodology

The approach to estimate PM2.5 and PM10 from multiple remote sensing data used in this study is shown in Fig. 2. All data collected were pre-processed for missing or invalid data before being matched spatially and temporally. The next step was to create new parameters from the main and auxiliary data to improve the stability of future models. Finally, all data were imported into Weka software for the attribute selection process and modeling by random forest algorithm. Furthermore, accuracy assessment was done using r, MAE, and RMSE. Besides, GIS software was used to create spatial distribution maps of estimated PM2.5 and PM10.

Fig. 2
figure 2

The flowchart of the approach to estimate PM2.5 and PM10 followed by this study

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Data pre-processing

Since the different spatial resolutions within the used datasets, only the pixel of the exact location of each ground station was extracted using the vector layer of ground stations imported in GEE. Regarding TROPOMI data, SO2 was missing for the winter months (December 2021 and January and February 2022), so it was excluded from heating season models. Furthermore, there are also some missing CO, HCHO, and NO2 data for the winter months due to the greater amount of cloudy days. Moreover, before July 2021, there was reported an error in the system for measuring AI so AI data from June 2021 was excluded from modeling, too. On the other hand, there were some missing and invalid (negative) in situ data, so those rows were removed from the dataset. Also, too high or too low ground measurements which differed too much from the rest of the dataset were deleted.

Data matching

Variables were matched spatially concerning each ground station’s location and temporally regarding each dataset’s temporal resolution. The Croatian National Network for Continuous Air Quality Monitoring Automatic ground stations measure atmospheric PM2.5 and PM10 every hour. On the other hand, TROPOMI captures daily images above Croatia between 10:00 AM and 1:00 PM CET. On that basis, TROPOMI data was matched with the in situ data of the exact hour. Furthermore, NOAA’s meteorological data are updated every 6 h (12:00 AM, 6:00 AM, 12:00 PM, and 6:00 PM) and were matched to the rest of the dataset based on the data for 12:00 PM. Geographical variables are temporally independent, so they were matched based only on location.

Covariates

The original parameters of Table 1 were expanded with the new ones (Table 2) based on the similarities they share to improve the models’ stability and to find out the composition and therefore possible sources of PM2.5 and PM10 in the observed period and between different biogeographical regions.

Table 2 Covariates created from remote sensing data (Mamić et al., 2022)
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Modeling PM2.5 and PM10

This study employed a random forest algorithm to estimate PM2.5 and PM10 values from multiple remote sensing data. The random forest algorithm was first proposed by Breiman (2001), and since that, it has been extremely successful as a general-purpose classification and regression method. The method, which combines several randomized decision trees and averages their predictions, has shown excellent performance in settings with a large number of variables compared to the number of observations. Furthermore, it is versatile enough to be applied to large-scale problems, adaptable to a variety of ad hoc learning tasks, and returns information on each variable importance (Biau & Scornet, 2016).

For the analyses, we used Weka (Waikato Environment for Knowledge Analysis), open-source software—first for the process of attribute selection and finally, for classification by random forest algorithm. Weka is a large collection of Java class libraries that implement a wide range of state-of-the-art machine learning and data mining algorithms (Witten et al., 1999). However, as mentioned above, prior to modeling, it was necessary to choose only the best parameters for each model. For that purpose, the Weka Classifier Subset Evaluator tool was used for the random forest algorithm with a percentage split of 70 to find the most important variables. Starting with the best-performing parameter, the Classifier Subset Evaluator progressively adds parameters to the model, one at a time, in order of their ranking. At each step, the accuracy of the classifier is measured on the test set using only the current subset of parameters. This process continues until the accuracy no longer improves. The Classifier Subset Evaluator is a useful way to reduce the dimensionality of the data while maintaining or even improving classification performance. By selecting only the most informative parameters, the method can improve the efficiency of machine learning algorithms and reduce overfitting. All remaining attributes were removed once the best had been identified, and then, a random forest classifier was used with data split into training and testing portions of 70% and 30%, respectively. Once the model was developed, it was saved, and all models in this study were trained separately by the same procedure described above. It is important to note that each model should use at least one parameter directly related to air pollution. The total number of instances and parameters to build each model is shown in Table 3.

Table 3 Number of instances and parameters used to create each model
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Accuracy assessment

The accuracy of the machine learning models may be validated using various metrics. However, the mean absolute error (MAE) and the root mean squared error (RMSE) are commonly used as evaluation metrics when estimating air pollutants (Hu et al., 2017; Larkin et al., 2017; Ayturan et al., 2018; Rybarczyk & Zalakeviciute, 2018; Van Roode et al., 2019; Masih, 2019).

MAE refers to the mean of the absolute error values of each prediction for all instances of the test dataset. The prediction error represents the difference between that case's actual and predicted value. MAE treats all individual differences with equal weight.

$$MAE= \sqrt{\sum\nolimits_{i=1}^{n}\frac{{|y}_{i}-{\widehat{y}}_{i}|}{n}}$$
(1)

The other metric used in this study is RMSE, as a standard way to measure the error of a model in predicting quantitative data. It's the square root of the average of squared differences between predicted and actual values.

$$RMSE= \sqrt{\sum\nolimits_{i=1}^{n}\frac{{({y}_{i}-{\widehat{y}}_{i})}^{2}}{n}}$$
(2)

In given equations \({\widehat{y}}_{1}, {\widehat{y}}_{2}, \dots ,{\widehat{y}}_{n}\) represent predicted values, \({y}_{1},{y}_{2}, \dots ,{y}_{n}\) are actual values, and \(n\) is the number of instances.

Results and discussions

Used variables

As said earlier, the composition of PM2.5 and PM10 varies due to the various climatic and geographical features. Also, between non-heating and heating seasons. Therefore, including multiple parameters in their estimation is crucial to improve the models’ stability and accuracy and discover the composition of specified air pollutants and their possible sources. The selected parameters used to develop models and estimate PM2.5 and PM10 seasonal values on the national and regional scale of Croatia are shown in Tables 4, 5, and 6.

Table 4 Parameters used to develop PM2.5 and PM10 seasonal models in the Continental region of Croatia
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Table 5 Parameters used to develop PM2.5 and PM10 seasonal models in the Mediterranean region of Croatia
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Table 6 Parameters used to develop PM2.5 and PM10 seasonal models in Croatia
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Looking at the attributes used to develop seasonal models of PM2.5 and PM10 for the Continental region of Croatia, it can be noticed that models use from six to 11 parameters. Furthermore, it is also noticeable how the PM2.5 models use more parameters than the PM10 models. Both PM2.5 models share four parameters: DEM, (CO + HCHO)/(CO-HCHO), WIND, and (WHT + O3)/(WHT-O3), while PM10 models share LST, SOIL_pH, and SLOPE parameters.

When looking at the attributes used to build seasonal models of PM2.5 and PM10 for Croatia’s Mediterranean region, it is noticeable that all models use from seven to 12 parameters, similar to those of the Continental region. Both PM2.5 models have three parameters in common: U-WIND, DEM, and (WHT + AI)/(WHT-AI), whereas PM10 models have five: CO, NO2, V-WIND, SLOPE, and (AI + DEM)/(AI + DEM) (AI-DEM). Since the Mediterranean region of Croatia is known for having strong winds, all four models incorporate at least one wind component parameter.

All national models used a similar number of attributes (8 to 10). For both PM2.5 models, CO, LST, U-WIND, and SLOPE parameters are used. On the other hand, both PM10 models share following parameters: NO2, SOIL_pH, DEM, SLOPE (AI + HUM)/(AI-HUM), and (WHT + AI)/(WHT-AI). Moreover, in all four models, the SLOPE parameter is used.

As previously noted, several studies to date have employed TROPOMI data to estimate atmospheric PM2.5 and PM10 concentrations. Therefore, to estimate PM2.5 and PM10 over China Wang et al. (2021) used numerous parameters (30) from multiple sources, such as TROPOMI (SO2, NO2, and O3), GEOS-FP (black carbon, organic carbon, nitrate, SO4, dust, ammonium, sea salt, humidity, air temperature, U-WIND, V-WIND, total precipitable water vapor, Pbl top pressure, surface pressure, planetary boundary layer height, air density at surface, surface velocity scale, and evaporation from turbulence), MODIS (NDVI, fractions of forest, savanna, grassland, cropland, urban, and arid land), Open Street Map (road density), and GPW (population density). Feature importance analysis done by Wang et al. (2021) showed that for PM2.5 estimation five the most significant variables are NO2, U-WIND, V-WIND, SO2, and Pbl top pressure. On the other hand, their analysis showed that for PM10 estimation five of the most significant variables are NO2, U-WIND, V-WIND, dust, and SO2. When it comes to the study by Han et al. (2022) who were estimating PM2.5 ambient concentrations for Thailand, they used only TROPOMI (AI, CO, HCHO, SO2, NO2, and O3) and NASA SRTM elevation data (DEM), and their feature importance analysis showed NO2 and SO2 as the most unsignificant parameters with both long-term (1 month) and short-term (10 days) dataset. On the other hand, CO and AI were chosen as the most significant parameters regarding both datasets. A study done by Son et al. (2022) also focused on estimating the PM2.5 concentrations in Thailand by combining multiple datasets: TROPOMI (CO, HCHO, SO2, NO2, and O3), ERA5 (temperature – T, dew-point temperature – Td, total evaporation, surface pressure, precipitation, U-WIND, V-WIND), ETOPO1 (DEM), and GlobCover (22 types of different land cover). Moreover, they approximated relative humidity and wind speed using Eqs. (3) and (4).

$$relhumidity=100* \frac{{e}^{\frac{17.625 *{ T}_{d}}{243.04 * {T}_{d}}}}{{e}^{\frac{17.625 * T}{243.04 * T}}}$$
(3)
$$windspeed= \sqrt{{U-WIND}^{2}+{V-WIND}^{2}}$$
(4)

Another study regarding PM2.5 estimation using TROPOMI data was done by Li et al. (2022) as a joint estimation of PM2.5 and O3 over China, and they used TROPOMI (HCHO and NO2), ERA5 (U-WIND, V-WIND, temperature, evaporation, total precipitation, surface pressure, surface, and top net solar radiation), CAMS (NO2, HCHO, NO, PM2.5, and O3). As opposed to other mentioned studies, Ahmed et al. (2022) used only TROPOMI data (AI, CH4, CO, HCHO, NO2, O3, SO2) to estimate average concentrations of PM2.5 in various cities in Pakistan.

Spatial distribution maps

Detecting PM2.5 and PM10 hotspots is essential in finding possible sources of air pollution. Therefore, based on the developed models and in situ data, using ArcGIS Pro 2.8.3 software and minimum curvature spline technique the interpolation maps of PM2.5 and PM10 were made on the national and regional scale of Croatia for both, non-heating and heating season (Figs. 3, 4, 5, 6, 7, and 8) to show the spatial distribution of the observed pollutants. The ground stations used for modeling are indicated as point data on the maps.

Fig. 3
figure 3

Interpolated PM2.5 concentrations for the Continental region of Croatia: A non-heating season from in situ data, B non-heating season from remote sensing data, C heating season from in situ data, and D heating season from remote sensing data

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Fig. 4
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Interpolated PM10 concentrations for the Continental region of Croatia: A non-heating season from in situ data, B non-heating season from remote sensing data, C heating season from in situ data, and D heating season from remote sensing data

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Fig. 5
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Interpolated PM2.5 concentrations for the Mediterranean region of Croatia: A non-heating season from in situ data, B non-heating season from remote sensing data, C heating season from in situ data, and D heating season from remote sensing data

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Fig. 6
figure 6

Interpolated PM10 concentrations for the Mediterranean region of Croatia: A non-heating season from in situ data, B non-heating season from remote sensing data, C heating season from in situ data, and D heating season from remote sensing data

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Fig. 7
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Interpolated PM2.5 concentrations for Croatia: A non-heating season from in situ data, B non-heating season from remote sensing data, C heating season from in situ data, and D heating season from remote sensing data

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Fig. 8
figure 8

Interpolated PM10 concentrations for Croatia: A non-heating season from in situ data, B non-heating season from remote sensing data, C heating season from in situ data, and D heating season from remote sensing data

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It is assumed that during the heating season, pollution will be higher. Furthermore, based on the geographical features of the Continental region, such as a relatively flat landscape and a climate of strong contrasts, it is assumed that it will be more polluted than the rest of the country.

The prediction maps are nearly identical to those created using in situ data. For the Continental region, the assumption that the heating season is more polluted was correct for PM2.5, but for PM10, the situation remained almost constant across seasons. On the other hand, for the non-heating season, PM2.5 pollution is low, with some mild hotspots visible in the southeast part of the region, whereas for the heating season, pollution is high and focused on the region’s west side. When it comes to PM10, in both seasons, pollution is highest in the far east of the region, with one hotspot around the urban area of the city of Osijek. There are two other noticeable hotspots in the central part of the region around the urban area of the city of Zagreb and the town of Kutina.

The assumption that the heating season is more polluted in the Mediterranean region was incorrect; again, predicted values appear to be slightly lower than actual ones and slightly underestimate them. Moreover, in the Mediterranean region, pollution is generally low. A single-polluted coastal area in the northern part is likely influenced by the urban area of the city of Rijeka, which is located directly above it. For both pollutants, there is a decrease during the heating season.

The interpolation maps on a national scale give us visual insight between the ground and remote sensing data and show the seasonal variations of PM2.5 and PM10 across the whole country. Prediction maps are nearly identical to those created using in-situ data. All conclusions drawn from regional maps are noticeable here. The Continental region stands out as the most polluted, with already mentioned hotspots located mostly in urban areas, and several studies (Fenger, 1999; Gulia et al., 2015; Kaplan et al., 2019) suggest that cities and high population densities are causes of air pollution. The low pollution in Alpine and Mediterranean regions supports other studies that talk about the impact of altitude (Kaplan & Avdan, 2020; Mamić, 2021; Ning et al., 2018) and sea (Rosenfeld et al., 2002) on air quality. Furthermore, seasonal changes in PM2.5 were also noticed in studies by Wang et al. (2021) and Li et al. (2022) who connected them with heating emissions and various meteorological conditions. Moreover, Wang et al. (2021) linked large PM10 emissions with sand storms and dry weather, which cannot be applicable to our study area since neither of these conditions is typical for any part of Croatia.

Validation

The PM2.5 and PM10 models on the national and regional scale of Croatia for heating and non-heating seasons were developed and are shown in Figs. 9, 10 with their correlation coefficient (r), mean absolute error (MAE) and root mean squared error (RMSE).

Fig. 9
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Regional PM2.5 and PM10 prediction models

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Fig. 10
figure 10

National PM2.5 and PM10 prediction models for Croatia

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The Continental region is regarded as the most polluted region in Croatia. All developed models have a high correlation that does not vary much with the seasons, making them highly stable. The accuracy of PM2.5 models is higher than that of PM10. On the other hand, the Mediterranean region is influenced by the sea and does not have high pollution. All developed models have a high correlation, which increases with the heating season. Similarly to models developed for the Continental region, the accuracy of PM2.5 models is higher than that of PM10.

The final objective of this research was to develop PM2.5 and PM10 models for the non-heating and heating season in Croatia using only open-source data from the GEE. The PM2.5 models for both seasons show a high correlation with r = 0.71 (MAE = 3.23 μg/m3 and RMSE = 5.27 μg/m3) for non-heating and r = 0.73 (MAE = 6.30 μg/m3 and RMSE = 8.82 μg/m3) for the heating season. PM10 models are a little less accurate, but still show moderate – r = 0.59 (MAE = 7.64 μg/m3 and RMSE = 13.33 μg/m3) for the non-heating season, to high correlation – r = 0.68 (MAE = 10.23 μg/m3 and RMSE = 15.02 μg/m3) for the heating season.

To better understand the developed national models, we can look at one ground station and its values (Fig. 11). This is the most western station located on the Istrian peninsula close to the sea and is installed on an industrial waste management site. Here we can see that the error between the actual and predicted values is the smallest for the PM2.5 non-heating season model and is close to 0 for this station. For this station, the error is the highest for the PM2.5 heating season model, even though this model has the highest accuracy among national models. The difference in seasons is clearly visible, where for the heating season we have higher values than in non-heating, as a justification for the seasonal temporal frame used in this research.

Fig. 11
figure 11

AMP Kaštijun ground station with actual and predicted data for all national models

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All PM2.5 and PM10 models developed in this research are shown in Fig. 12, with their r, MAE, and RMSE. The r of each developed model is ranked from highest (green) to lowest (red). Low MAE and RMSE values are represented by red arrows, while yellow and blue arrows, respectively, represent medium and high values. Therefore, it is clear that all PM2.5 models have shown better performance compared to PM10. Moreover, almost all regional models outperformed national ones.

Fig. 12
figure 12

All developed PM2.5 and PM10 models

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Comparison with CAMS data

The Copernicus Atmosphere Monitoring Service (CAMS) provides global and European data related to air pollution and health, greenhouse gas emissions, solar energy, and climate forcing. For the period observed by this research, available CAMS data is only that of PM2.5 at the spatial resolution of 44 528 m. The averaged PM2.5 data for the non-heating and heating season was collected for Croatia by GEE and compared with the in situ data and data predicted by developed models (Fig. 13).

Fig. 13
figure 13

PM2.5 comparison between CAMS, predicted and in situ data for Croatia: a non-heating season and b heating season

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For the non-heating season, the CAMS data appear higher than most stations’ actual values. On the other hand, the situation is the opposite for the heating season, where the CAMS data are underestimating the actual values. The CAMS data appear to be consistent throughout all stations and are unable to show sudden changes in the observed pollutant, which may be due to the lower spatial resolution of the data. All being said, CAMS is a valuable source of PM2.5 data. However, on this scale, the models developed in this study proved to be a better solution.

Conclusions

This study followed a new approach to estimate ambient concentrations of PM2.5 and PM10 from TROPOMI and other open-source remote sensing data available on GEE. Therefore, on a non-heating and heating seasons time scale, the random forest machine learning method was successfully used to create moderate to high precision PM2.5 and PM10 models for the Republic of Croatia. The spatial distribution of PM2.5 concentrations during the heating season revealed significant variations in pollution between biogeographical regions, which motivated us to develop regional models as well.

Due to the insufficient number of ground monitoring stations in the Alpine region of Croatia, it was decided not to develop models for the Alpine region. However, the Continental region has sufficient ground monitoring stations and is the most polluted region in the country. As a result of their high correlation and little seasonal variation, all models trained for the Continental region are quite stable. The accuracy of PM2.5 models (0.73 < r < 0.74) is higher than that of PM10 (r = 0.69). All developed models have a high correlation when it comes to the Mediterranean region, which is mainly influenced by the sea and strong winds. The accuracy of the models increases with the heating season, although pollution is lower in the heating season. The PM2.5 model shows r = 0.73 for the non-heating season and r = 0.82 for the heating season. On the other hand, the PM10 model has r = 0.67 for non-heating seasons and r = 0.73 for heating seasons. Similarly to models for the Continental region, the accuracy of PM2.5 models is higher than that of PM10.

Developed models for predicting seasonal variations of PM2.5 and PM10 on the whole territory of the Republic of Croatia show moderate to high accuracy. In particular, the PM2.5 model for the non-heating season has r = 0.71 and r = 0.73 for the heating season. On the other hand, the PM10 model has r = 0.59 for the non-heating, and r = 0.68 for the heating season.

Comparison between in situ, predicted and CAMS PM2.5 data have shown how CAMS is consistent, but unable to monitor sudden changes in the observed pollutant. Therefore, the developed models proved to be a better solution for monitoring atmospheric concentrations of PM2.5 and PM10 over Croatia.

Most of the models developed slightly underestimate the actual values, making the predicted values appear slightly lower. However, even in places with low levels of pollution, all models have demonstrated a general ability to estimate PM2.5 and PM10 levels. Additionally, all models can accurately identify all PM2.5 and PM10 hotspots. The approach proposed by this study has great potential to be extended to a larger scale. However, manual cleaning of the data set can be challenging, especially in emergency situations of atmospheric pollution; thus, for future studies, we recommend automatization of the process which seems to be a key element of modeling. Also, future studies should focus on applying the regional models developed by this research in other Continental and Mediterranean regions in Europe.