Adéla Holubová Šmejkalová This email address is being protected from spambots. You need JavaScript enabled to view it., Jáchym Brzezina 

Czech Hydrometeorological Institute, Na Šabatce 2050/17, 143 06 Prague 4-Komořany, Czech Republic


Received: March 14, 2022
Revised: June 24, 2022
Accepted: August 1, 2022

 Copyright The Author's institutions. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited. 


Download Citation: ||https://doi.org/10.4209/aaqr.220130  


Cite this article:

Šmejkalová, A.H., Brzezina, J. (2022). The Effect of Drought on PM Concentrations in the Czech Republic. Aerosol Air Qual. Res. 22, 220130. https://doi.org/10.4209/aaqr.220130


HIGHLIGHTS

  • A difference was found in PMx concentrations depending on the extent of drought.
  • A relation was found between the number of consecutive dry days and PM10 conc.
  • A correlation was found between PMx conc. on dry days and soil temperature and moisture.
 

ABSTRACT


The main focus of this study is the effect of drought on air quality in the Czech Republic. PMx (PM10 and PM2.5) concentrations at 21 air quality monitoring stations of different types (rural, suburban and urban) were studied during a 10-year period from 2010 to 2019. Only data measured during the vegetation period (April–September) were used. In addition, other meteorological factors were taken into account as well, in particular wind speed, air and soil temperature and soil moisture. It was found that PM10 concentrations were higher by 26.7 to 46.7% during dry periods with the difference being statistically significant. A strong correlation was found between PM10 and PM2.5 concentrations and the soil temperature and moisture, particularly at the top soil layer (0–10 cm depth). Results of the study show that soil moisture affects the extent of resuspension and soil erosion. Soil moisture values above 36% create unfavourable conditions for resuspension or erosion even when soil temperature is higher than 20°C.


Keywords: Air pollution, Drought, Particulate matter, Soil erosion


1 INTRODUCTION


The air quality in Europe has improved significantly over the last few decades (Butt et al., 2017; Colette et al., 2016). Implementing a new EU directive to member state laws (EEA, 2020) forced major emission emitters to modernize technologies and decrease total emissions. The results of the regulations reflecting EU directives are apparent in anthropogenic emission inventories over the last few decades (Colette et al., 2016; EEA, 2018). However, air quality depends not only on the actual amount of emitted pollutants, but also on meteorological conditions (Ding et al., 2021; Zhang et al., 2021) and several other factors, for example, reactions of chemical entities in the atmosphere (the so-called secondary reactions). With regard to meteorological conditions, for example low temperatures in combination with poor dispersion conditions during wintertime represent a very unfavorable condition often leading to significant increase in pollution levels—low temperatures increasing heating intensity and thus emissions from heating, poor dispersion conditions leading to accumulation of pollutants close to their sources and near ground level. (Cichowicz et al., 2017; Lv et al., 2020; Shi et al., 2015).

One of the consequences of the current ongoing climate change is a more frequent occurrence and intensity of heat and drought waves (more detailed is described e.g., in Perkins-Kirkpatrick and Lewis (2020)). These changes can also affect the concentrations of certain air pollutants. Several major heatwaves associated with dry periods were observed in Europe in the past decades, most recently in 2015 and 2018 (Lin et al., 2020). With regard to air quality, the most significant effect is associated with meteorological and agricultural drought. A meteorological drought can be quantified based on the precipitation amount. Agricultural drought is defined by the low availability of soil moisture which adversely limits the crop yield (Mannochi et al., 2004). An agricultural drought may cause damage to vegetation that presumably influences soil erosion.

Drought can affect air quality directly by increasing dust concentrations or changing the atmosphere's chemistry. Lack of precipitation reduces the ability of the atmosphere to decrease pollution levels by wet scavenging (Vicente-Serrano et al., 2020; Wang et al., 2017). In the past, many times drought resulted in large dust storms. Changes in the chemistry of the atmosphere caused by drought usually manifest themselves by changes in ground-level ozone concentrations, which is formed via photochemical reactions.

Drought can, however, also have an indirect effect on the concentrations of air pollutants. An example of an indirect effect of drought on air quality are forest fires, during which a significant amount of naturally produced emissions are released into the atmosphere (Vicente-Serrano et al., 2020). The impact of forest fires on air quality was reported in Bo et al. (2020). The sensitivity of fine dust to drought was demonstrated by Achakulwisut et al. (2018). This study suggests that drought, driven by climate change, is a potential risk for public health due to increased dust concentrations.

The relationship between drought and PMx concentrations was studied during the winter period (Hu et al., 2019; Liao et al., 2020). Our study, however, focuses on the effect of drought on PMx concentrations during the vegetation season to exclude the effect of increased PMx concentrations from heating emissions and because in the warm half-year the soil is not potentially frozen or covered with snow. The connection between soil temperature and moisture and PMx concentrations was also investigated as the soil is a potential source of aerosol particles. It was assumed that soil temperature influences PMx concentrations more significantly compared to air temperature in the non-heating season. Despite the fact that air temperature correlates with soil temperature (Islam et al., 2015; Yeşilirmak, 2014), they are not equal and the deeper in the soil one measures, the greater the difference can be. In general, soil temperature shows much less variability during the day as well as during the year than air temperature.

The presented study focuses on study influence of drought to air quality (namely PM10 and PM2.5 concentrations) in the Czech Republic. Since the other studies from our region aimed at drought and heat waves from a meteorological point of view (e.g., Trnka et al., 2016; Vautard et al., 2007), this kind of evaluation is unique in the Czech Republic. Main goal is to investigate some patterns of the relation between drought and PM10 concentrations to understand more deeply, what influence on air quality can be expected during dry periods. Based on theoretical knowledge the authors stated a hypothesis that drought could have potentially negative effects on air quality during the vegetation season.

 
2 METHODS


 
2.1 Station Selection

There is a well spatially distributed network of ambient air quality monitoring stations (National Air Quality Monitoring Network (NAQMN)) and meteorological and climatological stations operated by the Czech Hydrometeorological Institute in the Czech Republic. The high number of ambient air quality monitoring stations (approximately 200 stations) was filtered for the purposes of this study, and only stations with PM10 concentrations data over the entire period of analysis (between 2010 and 2019) were selected. The second criterion for station selection was that precipitation data from a professional meteorological station located within a radius of 200 m from the air quality monitoring point (ambient air quality stations themselves are not equipped with a rain gauge) is available. Given the very high spatial and temporal variability of precipitation, it is crucial only to select stations that are relatively close to each other. Given that precipitation data was taken from a location at most 200 m away from the air quality monitoring site, one can assume that the precipitation amount is equal or very similar to the precipitation amount that would be measured directly at the air quality measuring point. The above described selection criteria were satisfied by a total of 21 stations (Fig. 1).

Fig. 1. Spatial distribution of the ambient air quality monitoring stations data of which were used in this study. Circles show the position of stations with PM10 measurement with colour representing that particular station type. Rural stations are coloured green, suburban blue and urban stations red. Stations that in addition to PM10 also monitor PM2.5 concentrations are distinguished by a black dot in the middle of the circle. The map data were processed in ArcGis programme (ESRI, 2018).Fig. 1. Spatial distribution of the ambient air quality monitoring stations data of which were used in this study. Circles show the position of stations with PM10 measurement with colour representing that particular station type. Rural stations are coloured green, suburban blue and urban stations red. Stations that in addition to PM10 also monitor PM2.5 concentrations are distinguished by a black dot in the middle of the circle. The map data were processed in ArcGis programme (ESRI, 2018).

Ambient air quality monitoring stations within the NAQMN are classified based on the classification scheme for the purposes of Exchange of information (EoI) listed in the Council Decision 97/101/EC. This classification was implemented into the Air Quality Information System (AQIS) database, which collects and stores data from all the NAQMN stations. In the above table (Table 1) station type is represented by two letters separated by a slash. First letter indicates the type of station—traffic (T), industrial (I) or background (B). Second letter specifies the type of zone Urban (U), Suburban (S) or Rural (R) (CHMI, 2019a) (Table 1).

Table 1. List of analysed stations and their basic characteristics. 

 
2.2 Drought Specification

In meteorology there is currently no exact consensus as to how a dry day or rain episode should be defined. However, for a day to be classified as a “wet day” (rain), commonly used threshold values for daily precipitation amount are 0.1, 0.2 and 1.0 mm measured between 06–06 UTC (ČMeS, 2017). The actual threshold value may depend on rain gauge measurements resolution (WMO, 2018), or the purpose of the study, also taking into account the particular studied area. This amount can, therefore, vary from 0.1 to 5.0 mm (Reiser and Kutiel, 2009). The calculation of the long-term meteorological normals considers number of days with rainfall of ≥ 1.0 mm (WMO, 2017). This study uses a threshold value of 0.2 mm (rain day is a day with precipitation amount > 0.2 mm, dry day ≤ 0.2 mm). The threshold value of 0.2 mm was chosen to avoid rain gauge measurements resolution errors and experience; the same threshold value was used for example in the study Scafetta and Mazzarella (2021).

Apart from having an exact definition of a dry day, also an exact definition of a dry episode is needed. Since studying the effect of drought on PM10 concentration is the goal of this study, and the resuspension effect during drought is expected, the influence of moisturizing the ground by rain must be minimized. A dry episode was defined as five consecutive dry days (five consecutive days with a total precipitation amount of ≤ 0.2 mm on each particular day). A wet episode was defined as day or number of days with precipitation amount > 0.2 mm.

 
2.3 Data Measurement and Data Quality

PM10 and PM2.5 concentration data used in this study were measured within the NAQMN by both automated stations and samplers. Automated stations are equipped with analysers, which provide data in real-time. In contrast, samplers sample particles on a filter and these are subsequently transported to a laboratory, where a gravimetric determination is performed to determine PM concentrations (Table 2). The determined PM10 and PM2.5 concentration data fulfils the criteria according to the Annex 1 of Directive 2008/50/EC and Annex IV of Directive 2004/107/EC for minimum data availability of 90% (CHMI, 2019b). Meteorological data (in this case, precipitation, wind speed, soil temperature and soil moisture) was measured according to the WMO specifications.

Table 2. Currently used instrumentation for PM10 and PM2.5 measurement.

 
2.4 Period of Analysis

Level of air pollution (in this case, PM10 and PM2.5 concentrations) has gradually been decreasing over the last three decades in the Czech Republic. The amount of emissions released into the ambient atmosphere began to drop in 1990s in response to the new Act No. 309/1991 Coll. on air protection, coming into force. During the next several years, air quality protection was improved by adopting a new legislation (Act No. 76/2002 Coll and Act No. 201/2012 Coll), reflecting EU directives (CHMI, 2019a). Similarly to other gaseous pollutants emissions, the aerosol particle emissions also significantly decreased by tens of percent between 1990 and 2018 (Fig. 2). However, the actual measured concentrations are not directly proportional to the emission levels and instead also reflect other factors, such as meteorological conditions. The effect of meteorological conditions is manifested in the course of PM10 concentrations at stations in the Czech Republic between 2001 and 2016. After 1999, the PM10 and PM2.5 emissions were more or less similar. However, PM10 concentrations began to increase in 2001, and higher concentrations were observed until 2006; a similar situation was observed in 2010 (Fig. 3). Year 2010 was characterized by unfavourable dispersion conditions, especially in the winter. Unfavourable meteorological situation can be quantified by a characteristic referred to as a degree day (D21- defined as the sum of differences in the indoor temperatures (21°C) and the average daily outdoor temperatures on heating days). D21 reflects the effect of meteorological conditions on emissions; this is described in detail in CHMI (2020), (Fig. 3). The period of analysis in this study is the ten-year long interval between 2010–2019, where always a 6-month period between April and September was analysed in each year. The period of analysis of this study saw no major changes in legislation related to air quality that could have a significant effect on emissions.

Fig. 2. The course of PM2.5, PM10 and total suspended particle emissions (TSP) in the Czech Republic, 1990–2018. Source: CHMI (2019a).
Fig. 2.
 The course of PM2.5, PM10 and total suspended particle emissions (TSP) in the Czech Republic, 1990–2018. Source: CHMI (2019a).

Fig. 3. Course of PM10 average annual concentrations and annual heating season characteristic expressed as degree days (D21) in the Czech Republic between 2001–2017. Shaded regions represent years with increased PM10 concentrations. The individual series are shown for various station types specified in the legend. Adapted from: CHMI (2018).Fig. 3. Course of PM10 average annual concentrations and annual heating season characteristic expressed as degree days (D21) in the Czech Republic between 2001–2017. Shaded regions represent years with increased PM10 concentrations. The individual series are shown for various station types specified in the legend. Adapted from: CHMI (2018).

 
2.5 Drought and Wetness Ratio

Based on the criteria described in Chapter 2.2, a parameter Rd/w representing the ratio between mean PM10 concentrations in dry and wet period was calculated as:

 
2.6 Long-range Transport

Long-range transport was explored through backward trajectories calculated by the HYbrid Single-Particle Lagrangian Integrated Trajectory HYSPLIT_4 model (Draxler and Rolph, 2013). 72-hour backward trajectories were recalculated every 24 hours with Global Data Assimilation System (GDAS) meteorological data with 1° × 1° grid resolution used. The start time at 6:00 UTC was set to coordinate with the time when the daily PMx concentrations averages are counted. Since the resolution of the HYSPLIT model is smaller than the geographical distance of the stations, only one receptor site was selected - the station in Košetice, which represents the centre of the Czech Republic, height of the receptor was set to 500 m AGL. The total amount of 1748 backward trajectories were clustered into 6 clusters representing air masses with different origins.

 
2.7 Soil Temperature and Moisture

Soil temperature and moisture are only monitored at some meteorological stations. For the purposes of this study, a rural station in Košetice was used as it has a complete data series of soil temperature and moisture measurements as well as a complete series of PM10 and PM2.5 concentrations data.

Soil temperatures are taken at depths of 5, 10, 20, 50 and 100 cm. Soil moisture is measured for three different levels of 0–10 cm, 10–50 cm and 50–100 cm deep. It can be assumed that resuspension from the surface will mostly be affected by the moisture and temperature of the upper-most layer of the soil. The analysis therefore focused on the 0–10 cm depth data for soil moisture and 5 cm data for soil temperature.

The dataset used consisted of data from the exact same period, i.e., vegetation seasons (April–September) between 2010 and 2019. 3 RESULTS PM10 concentrations were generally higher in the dry period during the whole period of analysis. Differences between various station types did not exceed 16.0%. Rural and urban stations differed by 15.6%. The average Rd/w at rural stations was 141.2%, at suburban 134.5% and urban 125.6%. PM10 concentrations median at rural stations was 19.8 µg m–3 and 14.0 µg m–3, at suburban 21.4 µg m–3 and 15.9 µg m–3 and at urban 21.1 µg m–3 and 16.8 µg m–3 in dry and wet periods respectively. The lowest median at rural stations was measured at Churáňov station 12.0 µg m–3 and 8.0 µg m–3 in dry and wet periods. In contrast 26.8 µg m–3 and 19.4 µg m–3 was recorded at Tušimice station in dry and wet periods. The most manifested variability was at Stankov station (suburban) where interquartile span was 15 µg m–3 in dry period. Reuslt from urban station were very similar. Highest median was measured at Tábor station 23.1 µg m–3 in dry period and 17.5 µg m–3 in wet period (Table S1, Fig. 4).

Fig. 4. Overview of PM10 concentrations during wet and dry periods April–September 2010–2019. Light green – rural stations, dry period; dark green – rural stations, wet period; light blue – suburban stations, dry period; dark blue – suburban stations, wet period; light red – urban stations, dry period; dark red - urban stations, wet period.Fig. 4. Overview of PM10 concentrations during wet and dry periods April–September 2010–2019. Light green – rural stations, dry period; dark green – rural stations, wet period; light blue – suburban stations, dry period; dark blue – suburban stations, wet period; light red – urban stations, dry period; dark red - urban stations, wet period.

Relationship between the data measured in dry and wet periods at different types of stations was tested by the nonparametric Dunn test with Holm method adjustment (Dinno, 2017, 2015; Dunn, 1964), with the null hypothesis being that there is no difference between the groups. Overall, there were statistically significant differences in grouped data of PM10 concentrations for each station type between a dry and wet period (Table S2).

 
3 RESULTS


 
3.1 Monthly Variability

PM10 concentrations were highest in April at all station types. This is most likely due to occasional occurrence of cold days resulting in various extent of effect of domestic heating in this spring month. The effect of growing plants and crops on fields can be observed at rural stations.

The beginning of vegetation growth and low cover at fields was pronounced from April to May by a similar Rd/w 132.0 ± 3.0%. The lowest Rd/w in July (full vegetation cover) was reflected in the highest Rd/w 161.2, and 158.9% in August and September (Fig. 5). Suburban stations Rd/w ratio is more or less the same from April to May 124.4 ± 1.2%. Highest Rd/w was recorded in August at both rural and urban stations. Urban stations are characterized by a very small difference between PM10 concentrations during dry and wet period in May and June, also observed at suburban stations. Highest Rd/w was observed in August—regardless of the station type. The second-highest Rd/w was found in September.

Fig. 5. Monthly variability in PM10 concentrations for different station types. Light green - rural stations, dry period; dark green – rural stations, wet period; light blue – suburban stations, dry period; dark blue – suburban stations, wet period; light red – urban stations, dry period; dark red - urban stations, wet period.Fig. 5. Monthly variability in PM10 concentrations for different station types. Light green - rural stations, dry period; dark green – rural stations, wet period; light blue – suburban stations, dry period; dark blue – suburban stations, wet period; light red – urban stations, dry period; dark red - urban stations, wet period.

Detailed comparison of the differences between PM10 concentrations in dry and wet periods in the individual months and at the individual stations shows statistically significant differences. The highest percentage of statistically significant differences between dry/wet PM10 concentrations was observed in August and September at all types of stations; most cases were recorded at the suburban station type. Which months had the lowest number of statistically significant differences depends on the type of station—rural and urban stations showed no statistically significant differences in May and July, and only one significant result in April. For suburban stations there is one statistically significant dry/wet difference in April and May (Table S3).

 
3.2 Effect of Cumulative Drought and Wet

Effect of long-term drought or wet period on PM10 concentrations was analysed by looking at the dependency of PM10 concentrations on the number of consecutive days marked as either dry or wet. Data was evaluated only to the threshold value, which was represented by the 95th percentile of the total number of cumulative number of days with drought or with precipitation (see Chapter 2.2). Periods with consecutive number of days above this 95th percentile value were not evaluated. The cumulative threshold (95th percentile) value for the dry period was 10 days, and for the wet period 70 days. Time interval 0–10 or 0–70 day was divided into several categories in 2-day steps. Rest of the values above the threshold were not taken into account due to a low number of cases (not exceeding 14%) and to limit the influence of the extreme values. PM10 concentrations generally increased as the cumulative number of days with drought also increased. The increase is more apparent in the interquartile range, especially after the 5th day at rural and urban stations where a gradual growth of concentrations was recorded. Some fluctuations were observed at suburban stations, however, an increasing trend was also observed. Statistic significance was proved only at rural and suburban stations between groups of 1–5 and 5–10 cumulative days of drought (Table S4).

In contrast, an increasing number of consecutive wet days resulted in a general decrease in PM10 concentrations. This effect of precipitation was apparent at all station types by a decrease of PM10 concentrations at the beginning of the wet period (first three days). The dependency of PM10 concentrations on cumulative days with precipitation was most pronounced at rural stations. Suburban and urban stations showed some variability in the increasing number of consecutive wet days. Close to the threshold of 70 days, one can observe some small increase of PM10 concentrations. Another mechanism that probably influenced these values, were increasing local emissions or regional transport, which can overcome the wet scavenging effect of precipitation (Fig. 6). Statistic significance between groups of 1–35 and 35–70 cumulative days of wetness was manifested at all types of stations (Table S4).

Fig. 6. PM10 concentration dependency on cumulative number of dry and wet days. Station indication: light green – rural stations, dry period; dark green – rural stations, wet period; light blue – suburban stations, dry period; dark blue – suburban stations, wet period; light red – urban stations, dry period; dark red - urban stations, wet period.Fig. 6. PM10 concentration dependency on cumulative number of dry and wet days. Station indication: light green – rural stations, dry period; dark green – rural stations, wet period; light blue – suburban stations, dry period; dark blue – suburban stations, wet period; light red – urban stations, dry period; dark red - urban stations, wet period.

 
3.3 Meteorological Conditions

Some of the stations of analysis are also equipped with meteorological measurements, these data were evaluated to find out how meteorological conditions influence the PM10 concentrations during dry and wet periods. Data of wind speed and wind direction were available for 13 stations. PM10 concentrations and wind speed were compared by Spearman coefficient Rs. Results confirmed a weak relationship between these variables in both wet and dry period. Prevailing correlation was negative and Rs values were very similar for both these periods. Highest Rs was found at the Brno-Tuřany station, where Rs in dry period was –0.25 and in wet period –0.48 (Fig. S1). Dependence of PM10 concentrations on wind speed and wind direction was analysed for only 3 stations (Košetice – rural, Prague-Libuš – suburban and Tábor – urban)—one representative station for each station type. The Conditional Probability Function (CPF) was calculated and visualized by polar plots (Carslaw and Ropkins, 2012), a critical value was set to 75th percentile of PM10 concentrations. Results confirm major differences between dry and wet period at Košetice station, not at the Prague-Libuš and Tábor (Fig. S2). Comparison of PM10 concentrations and temperature measured at 2-meter height was available for 14 stations. Similar Rs results were found as in the case of wind (Fig. S3). These results are probably influenced by relatively low PM10 concentrations during the studied period compared to high concentrations in cold part of year when the relationship between PM10 concentrations and temperature can be expected to be much stronger. For example, Rs of PM10 concentration and temperature in winter (2010–2019) at the station in Košetice is –0.76.

 
3.4 PM2.5/PM10 Ratio

The measurement of PM2.5 concentrations is not performed at all stations included in this study. The ratio between PM2.5 and PM10 was thus calculated only for 13 stations where PM2.5 data were available. Since various pollution sources produce various ratios of fine (PM2.5) and coarse particles (PM10-2.5), the PM2.5/PM10 ratio can provide information about potential particle origin and its source. Anthropogenic sources, including primary and secondary particles, result in a higher PM2.5/PM10 ratio; in contrast, a relatively high ratio of coarse particles (primary particles) is produced by natural sources (for example soil erosion), manifested by a lower PM2.5/PM10 ratio (Spandana et al., 2021; Xu et al., 2017; Zhao et al., 2019). The PM2.5/PM10 ratio varies at the individual types of stations. There is no particular ratio typical for a specific station type (Munir, 2017; Putaud et al., 2010) for example, the ratio in the range 0.60 to 0.70 is characteristic for both rural and urban stations (Munir, 2017). In case of this study, in general, the PM2.5/PM10 ratio was lowest at rural stations and highest at urban stations (0.66 and 0.74 respectively). This means that a rural station had nearly 10% higher ratio of coarse particles than an urban station. Surprisingly the difference between dry and wet period PM2.5/PM10 ratios at individual station types was very small (< 1%), no statistically significant results was found (Fig. 7, Table S5).

Fig. 7. PM10 and PM2.5 concentrations and their ratio for a dry and wet period. Station indication: light green – rural station, dry period, dark green – rural station, wet period; light blue – suburban station, dry period; dark blue – suburban station, wet period; light red – urban station, dry period; dark red – urban station, wet period. White circles indicate PM2.5/PM10 ratio for a dry and wet period. Error bars show the standard deviation.Fig. 7. PM10 and PM2.5 concentrations and their ratio for a dry and wet period. Station indication: light green – rural station, dry period, dark green – rural station, wet period; light blue – suburban station, dry period; dark blue – suburban station, wet period; light red – urban station, dry period; dark red – urban station, wet period. White circles indicate PM2.5/PM10 ratio for a dry and wet period. Error bars show the standard deviation.

The PM2.5/PM10 ratio during the individual months shows similar patterns at all stations only in April and May when the dry period ratio is lower compared to the wet period. At rural stations, an apparent change occurred in June; after June, the dry period ratio indicates a higher amount of PM2.5 in the atmosphere than during the wet period. Analogous behaviour was observed at suburban and urban stations, but the change occurred one month later, in July. This change in the PM2.5/PM10 ratio between dry and wet period in the individual months may be due to the contribution of secondary particles in the air. While concentrations of coarse particles may be elevated or resuspended in April and May (in June at suburban and urban stations) during dry period because of the lack of vegetation cover on fields, secondary particles formation and other anthropogenic activities can contribute to fine particles in the summer months (Fig. 8). The PM2.5/PM10 ratio is very similar in September when the difference in the mean air temperature between dry and wet period is minimal (Fig. S4). Overall, the PM2.5/PM10 ratio observed in this study shows similar pattern as was reported in Gehrig and Buchmann (2003) where secondary increase of this ratio in summer months was recorded.

Fig. 8. PM2.5/PM10 ratio for the individual months during the dry and wet period. Lines represent median, bars indicate 5th and 95th percentile (bars with borders show results of dry period). Station indication: light green – rural stations, dry period; dark green – rural stations, wet period; light blue – suburban stations, dry period; dark blue – suburban stations, wet period, light red – urban stations, dry period, dark red - urban wet season.Fig. 8. PM2.5/PM10 ratio for the individual months during the dry and wet period. Lines represent median, bars indicate 5th and 95th percentile (bars with borders show results of dry period). Station indication: light green – rural stations, dry period; dark green – rural stations, wet period; light blue – suburban stations, dry period; dark blue – suburban stations, wet period, light red – urban stations, dry period, dark red - urban wet season.

 
3.5 Effect of Long-range Transport

PM10 concentrations during dry and wet periods were analysed also in relation to long-range transport (PM2.5 analyses were excluded for lack of data and small representativeness). Each of the six clusters was of continental origin. Clusters number 2 and 4 were aged air masses, cluster number 3 was fresh air mass (Fig. 9(a)). The highest PM10 concentrations were measured in cluster number 6, during both periods. The increased contribution of pollution was probably caused by emissions from the region around Austria's capital. Lower concentrations in cluster number 3 prove the freshness of this air mass (Fig. 9, Table 3). The influence of air masses of different origins on the concentration of PM10 in dry and wet periods has not been proven. There are no statistically significant differences between PM10 concentrations in the individual clusters in dry and wet periods (Table S6). Wilcox-Mann-Whitney test was used for this comparison (Bauer, 1972; Hollander et al., 2013).

Fig. 9. Statistical cluster analysis of air mass backward trajectories showing dependency of PM10 concentrations in dry and wet periods on different air mass origins at rural, suburban and urban stations. Receptor site Košetice station was selected as a representative of the centre of the Czech Republic. Each cluster has its own color, black horizontal line represents the median, borders of boxes show 25th and 75th percentile, error bars indicate the overall minimum and maximum valuesFig. 9. Statistical cluster analysis of air mass backward trajectories showing dependency of PM10 concentrations in dry and wet periods on different air mass origins at rural, suburban and urban stations. Receptor site Košetice station was selected as a representative of the centre of the Czech Republic. Each cluster has its own color, black horizontal line represents the median, borders of boxes show 25th and 75th percentile, error bars indicate the overall minimum and maximum values.

Table 3. Overview of PM10 concentrations in different air masses clusters during dry and wet period on rural, suburban and urban stations.

 
3.6 Soil Temperature and Moisture

The analysis looked at the PM10 and PM2.5 concentrations and their relationship with soil temperature and moisture as such, regardless of wet or dry period as it can be assumed that soil moisture on its own is an indicator of dryness or wetness. The relationship between air temperature and soil temperature at the individual depths was evaluated by Rs (Table S7) and is visualized in Fig. S5. Further evaluation is focused only on the effect of soil moisture (Hs) and soil temperature (Ts) on PMx concentrations. The first part was devoted to their individual relations. Overall soil moisture at the Košetice station ranged from 13 to 74% Distribution of soil moisture and soil temperature values is plotted in Fig. S6.

The soil temperature (Ts) values ranged from 0.9 to 25.0°C. Ts data were rounded to an integer, and data below 5.0°C were excluded from further analyses as there is a probability that high PM10 and PM2.5 concentrations at very low temperatures (Ts < 5.0°C) are associated with domestic heating. The impact of moistured soil on reduced particle resuspension is apparent in a decrease of both PM10 and PM2.5 concentrations when Hs exceeded 30.0% (more pronounced in case of PM2.5 concentrations) (Figs. 10(a) and 10(c)). PM10 average concentration was 17.0 µg m–3 (Hs < 30.0%) and 15.6 µg m–3 (Hs ≥ 30.0%), PM2.5 average concentration 11.3 µg m–3 (Hs < 30.0%) and 10.4 µg m–3 (Hs ≥ 30.0%). Statistically significant differences were proved between the data series (Table S8). PM10 and PM2.5 concentrations stop fluctuating and start increasing when Ts exceeds 15.0°C (Figs. 10(b) and 10(d)). PM10 average concentration was 16.0 µg m–3 (Ts < 15.0°C) and 16.3 µg m–3 (Ts ≥ 15.0°C). Surprisingly, opposite behaviour was observed for PM2.5 with an average concentration of 11.1 µg m–3 (Ts < 15.0°C) and 10.6 µg m–3 (Ts ≥ 15.0°C). Only PM2.5 concentrations were statistically significantly different under diverse Ts conditions (Table S8). The situation is clearer when the conditions are focused on only part of the data where PM10 and PM2.5 concentrations start to decrease (in case of Hs ≥ 36.0%) or increase (in case of Ts ≥ 20.0°C) (Fig. 10). Average concentrations are listed in (Table 4).

Fig. 10. Boxplots of soil moisture (Hs0–10cm) and soil temperature (Ts0–5cm) during different levels of PM10 and PM2.5 concentrations. Black horizontal line is median, borders of boxes show 25th and 75th percentile, error bars indicate minimum and maximum values.Fig. 10. Boxplots of soil moisture (Hs0–10cm) and soil temperature (Ts0–5cm) during different levels of PM10 and PM2.5 concentrations. Black horizontal line is median, borders of boxes show 25th and 75th percentile, error bars indicate minimum and maximum values.

Table 4. Average PM10 and PM2.5 concentrations under different Hs and Ts conditions.

Since the results of PM10 and PM2.5 under set soil conditions were statistically significant different (Table S9), data series were split into a combination of soil conditions (Hs ≥ 36.0% Ts ≥ 20.0°C, Hs ≥ 36.0% Ts < 20.0°C, Hs < 36.0% Ts < 20.0°C, Hs < 36.0% Ts ≥ 20.0°C) to investigate which soil conditions influence the PMx concentration the most. PM2.5 concentrations have insufficient data available for this analysis, thus only PM10 results were used. High PM10 concentrations at high temperatures can be explained as the result of drought and resuspension. Data series of PM10 concentration at soil moistures Hs < 36.0% Ts ≥ 20.0°C differs from the rest of the analysed Hs and Ts combinations on a statistically significant level (Table S10).

This is a clear indicator that high PM10 concentrations are the result of dry conditions and the associated resuspension or soil erosion Moreover, results were further proved by the fact that at high temperatures, the concentrations are lower at higher soil moisture values (e.g., when temperatures are above 20°C and soil moisture is higher than 36%, average PM10 concentration is only 16.3 µg m–3) (Fig. 11).

Fig. 11. Boxplots of PM10 concentrations during different levels of soil moisture (Hs0–10cm) and soil temperature (Ts0–5cm). Black horizontal line is median, borders of boxes show 25th and 75th percentile, error bars indicate minimum and maximum values.Fig. 11. Boxplots of PM10 concentrations during different levels of soil moisture (Hs0–10cm) and soil temperature (Ts0–5cm). Black horizontal line is median, borders of boxes show 25th and 75th percentile, error bars indicate minimum and maximum values.


4 DISCUSSION


The relationship between drought and air pollution has already been studied in other scientific papers. For example, a study by Achakulwisut et al. (2018) focused on the relationship between fine dust concentration and the 2-month Standardized Precipitation Evapotranspiration Index (SPEI02) anomalies. Similarly to the presented study, a correlation and a negative relationship between the two parameters was found. The study talks about the effects of soil moisture on wind erosion in a controlled experiment in a wind tunnel and it was found that the wind speed threshold increases with soil moisture—i.e., at a higher soil moisture value, a higher wind speed value (threshold) is required for erosion to occur. The presented study did not look at the correlation between wind speed and soil moisture directly, however, when looking at the results of this study, one can see that at higher temperatures (> 20°C), PM2.5 concentrations were indeed higher at lower soil moisture values as well.

Another study published in 2018 (Doede and Davis, 2018) and conducted in California, found similar results to the ones in this paper, showing a correlation between PM10 concentrations and drought effects. In this particular study, this was partly due to the drying out of lakes, where the exposed lakebed, which is normally submerged under water, can be a source of PM10 particles.

Charusombat and Niyogi (2011) focused on the topic of drought vs air pollution using satellite observations, looking particularly at the variability between aerosol optical depth (AOD) retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) and in situ particular matter (PM2.5 and PM10) concentrations. Level of drought was represented by the Standardized Precipitation Index (SPI). The results clearly indicated higher AOD values under drought conditions during summer periods. This study also looked at the differences between urban and rural sites. It was found that in agricultural areas the correlation between PM10 concentrations and drought is higher than in urban areas during drought periods, which is in accordance with the results of this study, where the most profound difference was observed at rather remote, agricultural sites than in urban locations.

Javadian et al. (2019) looked at the extreme case of dust storms, which are common in semi-arid and arid regions. Such storms have an extreme effect on particulate matter concentrations and a correlation between drought events and extreme dust events was therefore found. Such storms in such an extent do not occur in the Czech Republic, but the results of the paper clearly demonstrate the possible effects of wind and resuspension that is more profound during drought times.

Exploration of the link between droughts and atmospheric aerosols was also studied by Charusombat and Niyogi (2011) using data from Indiana, U.S. Authors of this study also divided the stations based on their type (urban, suburban, rural) and a potentially more significant link during drought conditions was found in case of the rural stations, which is in accordance with the results found in this study based on data from the Czech Republic.

Overall, the results found in this study are in concordance with other papers that concentrated on this topic. This is despite the fact these studies were performed in other parts of the world, where the effect of local conditions such as vegetation type, climate type, could play a role.

The original hypothesis that assumed a potential negative effect of drought on suspended particle concentrations during the vegetation season was confirmed.

Further analyses of the effects of drought on air pollution could for example focus on the effects of drought on ground-level ozone pollution. This phenomenon was studied for example by Demetillo et al. (2019), indicating that drought conditions potentially alter multiple terms in the ozone mass balance equation.

Another possible extension of the study in this field would be to perform special-purpose measurements of air pollution and meteorological conditions at sites, where a significant effect of drought could be expected (agricultural, open areas). The presented study only uses data from long-term stationary ambient air quality monitoring stations within the Czech ambient air quality monitoring stations network.

 
5 CONCLUSION


The presented study focuses on the comparison between PM10 and PM2.5 concentrations during dry and wet periods at different types of stations (rural, suburban, urban) across the Czech Republic. Data were analysed from 2010 to 2019 for vegetation season (April–September) to avoid the major influence of emissions from domestic heating during the heating season. The parameter Rd/w (ratio of PM10 concentrations between dry and wet period) varies from 126.7 to 146.7%, which confirms higher PM10 concentrations during dry periods. Dunn test proved there is a statistically significant difference between the PM10 concentrations at each station type for dry and wet period.

The effect of vegetation cover could be observed by the lowest Rd/w in July (full vegetation cover) and highest Rd/w in August. August is a month when both harvest and temperatures peak, so the resuspension of soil particles is most intense. Data from the individual stations in August proved a statistically significant difference between concentrations in dry and wet periods. The effect of consecutive dry or wet days on increasing/decreasing PM10 concentrations was also apparent. PM10 concentrations gradually increased after 5th day of consecutive drought at rural and urban stations. Significant decrease in PM10 concentrations was observed especially at the beginning of a wet period (0–2 day). The effect of cumulative wet days on concentration decrease was pronounced most at rural stations.

The PM2.5/PM10 concentration ratio in both dry and wet periods was very similar, the difference being less than 1%. In the individual months PM2.5/PM10 ratio varies between different station types. While at the beginning of a vegetation season, a smaller ratio indicates a prevailing amount of PM10 particles at all station types, in the following months the situation was different. A higher PM2.5/PM10 ratio (indicating higher ratio of PM2.5 particles) was observed during the dry period from June at rural, and July at suburban and urban stations. This fact is probably caused by secondary particles formation and other anthropogenic activities (e.g., increased traffic, barbecue) that can contribute to fine particles in the summer months, thus changing the PM2.5/PM10 ratio.

A significant effect of soil condition on PMx concentrations was proved by analysing soil moisture (at a depth of 0–10 cm) and soil temperature at a depth of 5 cm underground. Soil moisture itself is a parameter that well represents the actual dryness of the soil. The effect of dried out soil was seen in a correlation between soil moisture and resuspension intensity or soil erosion. Soil with moisture above 36% suppressed resuspension even if soil temperature was higher than 20°C.


REFERENCES


  1. Achakulwisut, P., Mickley, L.J., Anenberg, S.C. (2018). Drought-sensitivity of fine dust in the US Southwest: Implications for air quality and public health under future climate change. Environ. Res. Lett. 13, 054025. https://doi.org/10.1088/1748-9326/aabf20

  2. Bauer, D.F. (1972). Constructing Confidence Sets Using Rank Statistics. J. Am. Stat. Assoc. 67, 687–690. https://doi.org/10.1080/01621459.1972.10481279

  3. Bo, M., Mercalli, L., Pognant, F., Cat Berro, D., Clerico, M. (2020). Urban air pollution, climate change and wildfires: The case study of an extended forest fire episode in northern Italy favoured by drought and warm weather conditions. Energy Rep. 6, 781–786. https://doi.org/10.1016/​j.egyr.2019.11.002

  4. Butt, E.W., Turnock, S.T., Rigby, R., Reddington, C.L., Yoshioka, M., Johnson, J.S., Regayre, L.A., Pringle, K.J., Mann, G.W., Spracklen, D. V. (2017). Global and regional trends in particulate air pollution and attributable health burden over the past 50 years. Environ. Res. Lett. 12, 104017. https://doi.org/10.1088/1748-9326/aa87be

  5. Carslaw, D.C., Ropkins, K. (2012). openair — An R package for air quality data analysis. Environ. Modell. Software 27–28, 52–61. https://doi.org/10.1016/j.envsoft.2011.09.008

  6. Česká meteorologická společnost (ČMeS) (2017). Elektronický meteorologický slovník výkladový a terminologický (eMS).

  7. Charusombat, U., Niyogi, D. (2011). Exploring the link between droughts and atmospheric aerosol loading, in: 2011 Symposium on Data-Driven Approaches to Droughts, Purdue University, West Lafayette, Indiana.

  8. Cichowicz, R., Wielgosiński, G., Fetter, W. (2017). Dispersion of atmospheric air pollution in summer and winter season. Environ. Monit. Assess. 189, 605. https://doi.org/10.1007/s10661-017-6319-2

  9. Colette, A., Aas, W., Banin, L., Braban, C.F., Ferm, M., González Ortiz, A., Ilyin, I., Mar, K., Pandolfi, M., Putaud, J.P., Shatalov, V., Solberg, S., Spindler, G., Tarasova, O., Vana, M., Adani, M., Almodovar, P., Berton, E., Bessagnet, B., Bohlin-Nizzetto, P., Boruvkova, J., Breivik, K., et al. (2016). Air pollution trends in the EMEP region between 1990 and 2012 EMEP/CCC-Report 1/2016; Joint Report of : EMEP Task Force on Measurements and Modelling (TFMM), Chemical Co-ordinating Centre (CCC), Meteorological Synthesizing Centre-East (MSC-E), Meteorologi. Kjeller.

  10. Czech Hydrometeorological Institute (CHMI) (2018). Air pollution in the Czech Republic in 2017. Czech Hydrometeorological Institute, Czech Republic.

  11. Czech Hydrometeorological Institute (CHMI) (2019a). Air pollution in the Czech Republic in 2018. Czech Hydrometeorological Institute, Czech Republic.

  12. Czech Hydrometeorological Institute (CHMI) (2019b). Commentary on the summary annual tabular survey. Czech Hydrometeorological Institute, Czech Republic.

  13. Czech Hydrometeorological Institute (CHMI) (2020). Data collection , processing and evaluation system. Czech Hydrometeorological Institute, Czech Republic.

  14. Demetillo, M.A.G., Anderson, J.F., Geddes, J.A., Yang, X., Najacht, E.Y., Herrera, S.A., Kabasares, K.M., Kotsakis, A.E., Lerdau, M.T., Pusede, S.E. (2019). Observing severe drought influences on ozone air pollution in California. Environ. Sci. Technol. 53, 4695–4706. https://doi.org/​10.1021/acs.est.8b04852

  15. Ding, J., Dai, Q., Li, Y., Han, S., Zhang, Y., Feng, Y. (2021). Impact of meteorological condition changes on air quality and particulate chemical composition during the COVID-19 lockdown. J. Environ. Sci. (China) 109, 45–56. https://doi.org/10.1016/j.jes.2021.02.022

  16. Dinno, A. (2015). Nonparametric pairwise multiple comparisons in independent groups using dunn’s test. Stata J. 15, 292–300. https://doi.org/10.1177/1536867X1501500117

  17. Dinno, A. (2017). Package ‘dunn.test.’ CRAN Repository

  18. Doede, A., Davis, R. (2018). Use of airborne PM10 concentrations at air quality monitoring sites in Imperial County, California, as an indication of geographical influences on lung health during drought periods: A time-series analysis. Lancet Planet. Heath 2, S10. https://doi.org/​10.1016/s2542-5196(18)30095-0

  19. Draxler, R.R., Rolph, G.D. (2013). HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory). NOAA Air Resources Laboratory, Silver Spring, MD.

  20. Dunn, O.J. (1964). Multiple comparisons using rank sums. Technometrics 6, 241–252. https://doi.org/10.1080/00401706.1964.10490181

  21. ESRI (2018). ArcGIS 10.6.

  22. European Environment Agency (EEA) (2018). European Union emission inventory report 1990-2016. EEA Report No 6/2018. Publications Office of the European Union, Luxembourg. https://doi.org/10.2800/571876

  23. European Environment Agency (EEA) (2020). Air quality in Europe - 2020 report. EEA Report. ublications Office of the European Union, Luxembourg. https://doi.org/10.2800/786656

  24. Gehrig, R., Buchmann, B. (2003). Characterising seasonal variations and spatial distribution of ambient PM10 and PM2.5 concentrations based on long-term Swiss monitoring data. Atmos. Environ. 37, 2571–2580. https://doi.org/10.1016/S1352-2310(03)00221-8

  25. Hollander, M., Wolfe, D.A., Chicken, E. (2013). Nonparametric statistical methods. John Wiley & Sons.

  26. Hu, Y., Wang, S., Yang, X., Kang, Y., Ning, G., Du, H. (2019). Impact of winter droughts on air pollution over Southwest China. Sci. Total Environ. 664, 724–736. https://doi.org/10.1016/​j.scitotenv.2019.01.335

  27. Islam, K.I., Khan, A., Islam, T. (2015). Correlation between atmospheric temperature and soil temperature: A case study for Dhaka, Bangladesh. Atmos. Clim. Sci. 05, 200–208. https://doi.org/10.4236/acs.2015.53014

  28. Javadian, M., Behrangi, A., Sorooshian, A. (2019). Impact of drought on dust storms: Case study over Southwest Iran. Environ. Res. Lett. 14, 124029. https://doi.org/10.1088/1748-9326/ab574e

  29. Liao, Z., Xie, J., Fang, X., Wang, Y., Zhang, Y., Xu, X., Fan, S. (2020). Modulation of synoptic circulation to dry season PM2.5 pollution over the Pearl River Delta region: An investigation based on self-organizing maps. Atmos. Environ. 230, 117482. https://doi.org/10.1016/j.​atmosenv.2020.117482

  30. Lin, M., Horowitz, L.W., Xie, Y., Paulot, F., Malyshev, S., Shevliakova, E., Finco, A., Gerosa, G., Kubistin, D., Pilegaard, K. (2020). Vegetation feedbacks during drought exacerbate ozone air pollution extremes in Europe. Nat. Clim. Change 10, 444–451. https://doi.org/10.1038/s41558-020-0743-y

  31. Lv, Z., Wei, W., Cheng, S., Han, X., Wang, X. (2020). Meteorological characteristics within boundary layer and its influence on PM2.5 pollution in six cities of North China based on WRF-Chem. Atmos. Environ. 228, 117417. https://doi.org/10.1016/j.atmosenv.2020.117417

  32. Mannochi, F., Todisco, F., Vergni, L. (2004). Agricultural Drought: Indices, Definition and Analysis, in: Rodda, J.C., Ubertini, L. (Eds.), The Basis of Civilization--water Science? International Association of Hydrological Sciences, p. 342.

  33. Munir, S. (2017). Analysing temporal trends in the ratios of PM2.5/PM10 in the UK. Aerosol Air Qual. Res. 17, 34–48. https://doi.org/10.4209/aaqr.2016.02.0081

  34. Perkins-Kirkpatrick, S.E., Lewis, S.C. (2020). Increasing trends in regional heatwaves. Nat. Commun. 11, 1–8. https://doi.org/10.1038/s41467-020-16970-7

  35. Putaud, J.P., Van Dingenen, R., Alastuey, A., Bauer, H., Birmili, W., Cyrys, J., Flentje, H., Fuzzi, S., Gehrig, R., Hansson, H.C., Harrison, R.M., Herrmann, H., Hitzenberger, R., Hüglin, C., Jones, A.M., Kasper-Giebl, A., Kiss, G., Kousa, A., Kuhlbusch, T.A.J., Löschau, G., et al. (2010). A European aerosol phenomenology - 3: Physical and chemical characteristics of particulate matter from 60 rural, urban, and kerbside sites across Europe. Atmos. Environ. 44, 1308–1320. https://doi.org/10.1016/j.atmosenv.2009.12.011

  36. Reiser, H., Kutiel, H. (2009). Rainfall uncertainty in the Mediterranean: Definitions of the daily rainfall threshold (DRT) and the rainy season length (RSL). Theor. Appl. Climatol. 97, 151–162. https://doi.org/10.1007/s00704-008-0055-z

  37. Scafetta, N., Mazzarella, A. (2021). On the rainfall triggering of phlegraean fields volcanic tremors. Water (Switzerland) 13, 154. https://doi.org/10.3390/w13020154

  38. Shi, G., Chen, Z., Teng, J., Li, Y. (2015). Spatio-temporal variation of total mercury in precipitation in the largest industrial base in China: impacts of meteorological factors and anthropogenic activities. Tellus B 67, 25660. https://doi.org/10.3402/tellusb.v67.25660

  39. Spandana, B., Srinivasa Rao, S., Upadhya, A.R., Kulkarni, P., Sreekanth, V. (2021). PM2.5/PM10 ratio characteristics over urban sites of India. Adv. Space Res. 67, 3134–3146. https://doi.org/​10.1016/j.asr.2021.02.008

  40. Trnka, M., Balek, J., Štepánek, P., Zahradnícek, P., Možný, M., Eitzinger, J., Žalud, Z., Formayer, H., Turna, M., Nejedlík, P., Semerádová, D., Hlavinka, P., Brázdil, R. (2016). Drought trends over part of Central Europe between 1961 and 2014. Clim. Res. 70, 143–160. https://doi.org/​10.3354/cr01420

  41. Vautard, R., Yiou, P., D’Andrea, F., de Noblet, N., Viovy, N., Cassou, C., Polcher, J., Ciais, P., Kageyama, M., Fan, Y. (2007). Summertime European heat and drought waves induced by wintertime Mediterranean rainfall deficit. Geophys. Res. Lett. 34, L07711. https://doi.org/​10.1029/2006GL028001

  42. Vicente-Serrano, S.M., Quiring, S.M., Peña-Gallardo, M., Yuan, S., Domínguez-Castro, F. (2020). A review of environmental droughts: Increased risk under global warming? Earth-Science Rev. 201, 102953. https://doi.org/10.1016/j.earscirev.2019.102953

  43. Wang, Y., Xie, Y., Dong, W., Ming, Y., Wang, J., Shen, L. (2017). Adverse effects of increasing drought on air quality via natural processes. Atmos. Chem. Phys. 17, 12827–12843. https://doi.org/10.5194/acp-17-12827-2017

  44. World Meteorological Organization (WMO) (2017). Guidelines on the Calculation of Climate Normals. WMO-No. 1203, World Meteorological Organization, Switzerland.

  45. World Meteorological Organization (WMO) (2018). Guide to Instruments and Methods of Observation. World Meteorological Organization, Switzerland.
  46. Xu, G., Jiao, L., Zhang, B., Zhao, S., Yuan, M., Gu, Y., Liu, J., Tang, X. (2017). Spatial and temporal variability of the PM2.5/PM10 ratio in Wuhan, Central China. Aerosol Air Qual. Res. 17, 741–751. https://doi.org/10.4209/aaqr.2016.09.0406

  47. Yeşilirmak, E. (2014). Soil temperature trends in Büyük Menderes Basin, Turkey. Meteorol. Appl. 21, 859–866. https://doi.org/10.1002/met.1421

  48. Zhang, Y., Ma, Z., Gao, Y., Zhang, M. (2021). Impacts of the meteorological condition versus emissions reduction on the PM2.5 concentration over Beijing–Tianjin–Hebei during the COVID-19 lockdown. Atmos. Ocean. Sci. Lett. 14, 100014. https://doi.org/10.1016/j.aosl.2020.100014

  49. Zhao, D., Chen, H., Yu, E., Luo, T. (2019). PM2.5/PM10 ratios in eight economic regions and their relationship with meteorology in China. Adv. Meteorol. 2019, 5295726. https://doi.org/​10.1155/2019/5295726


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