1 Introduction

The exposure and inhalation of polluted air can lead to a wide range of health issues, including allergic rhinitis, sore throats, chronic coughs, bronchitis, sinusitis, chest pain and bronchial asthma [1,2,3,4,5,6]. The risks of exposure to particulate matter are considerable in both indoor and outdoor contexts, which raises serious concerns about the consequences on human health [7]. The respiratory and cardiovascular systems may be negatively affected by the airborne particulate matter (PM) [8,9,10]. The levels of respirable particulate matters reduce lung capacity, aggravates asthma, raises the risk of pneumonia and considerably raises the newborn mortality rate [11, 12]. The respiratory system can be harmed directly or indirectly by hazardous organic and inorganic gases, volatile chemicals, and trace metals found in airborne particulate matter [13]. About 4.9 million people died as a result of air pollution worldwide (8.7% of all deaths), with India accounting for 1.2 million of those deaths [14]. In West Bengal, air pollution caused 50.9% of all fatalities in people under the age of 70 [15]. On March 11, 2020, the World Health Organization (WHO) declared the COVID-19 outbreak a pandemic [16]. In response, countries had to go into lockdown to stop the spread of the virus [17]. The Indian government first issued a one-day voluntary curfew on March 22, 2020 and then tightly implemented a first lockdown from March 24 to April 14, 2020 [18]. Afterward, industrial, commercial and transportation activities were shut down except emergency services to prevent private and public gatherings. The abrupt cessation of transportation and industrial operations led to a gradual reduction in air pollution. In heavily polluted megacities like Delhi and Kolkata, air pollution levels, except ozone decreased significantly during the lockdown period. The distribution of air pollutants in the atmosphere is influenced not only by industrial, commercial and transportation activities but also by meteorological factors. The primary meteorological factors such as temperature, relative humidity, wind speed, wind direction and rainfall significantly impact on air pollution distribution [8, 19]. The various studies have reported the considerable impact of these meteorological factors on air pollution levels in the ambient atmosphere [20,21,22]. Temperature inversions can trap pollutants close to the ground, wind can disperse or concentrate pollutants, and rainfall can wash out pollutants from the atmosphere. Studies from all over the world demonstrated dramatic shifts in pollution levels, particularly in the air with the exception of ozone [23,24,25], as well as the positive effects on health such as a reduction in mortality, morbidity, premature deaths, chronic obstructive pulmonary disease (COPD), disability adjusted life years (DALY) and other conditions [26,27,28]. West Bengal, one of the most populous states of India, from both industrial (zones: Durgapur, Asansol, Haldia, and Howrah) and traffic-related sources of air pollution (zones: Kolkata, Howrah, Siliguri). Kolkata, the capital of West Bengal, has a large economy, a dense population, and significant air pollution from transportation. In south West Bengal, Howrah and Haldia's industrial activities contribute to air pollution. The two largest industrial cities in eastern India are Durgapur and Asansol. An important city in North Bengal is Siliguri, which serves as the entrance to north-east India. The primary objectives of this study are to comprehensively investigate the air quality dynamics in multiple cities across the state of West Bengal, India from 2019 to 2021. Furthermore, the research will delve into the long- and short-term health effects on human well-being, specifically related to particulate matter, focusing on PM10 and PM2.5. In this study investigate the mortality due to lung cancer (LC), ischemic heart disease (IHD), stroke, and chronic obstructive pulmonary disease (COPD). In children aged 0–5 years, the study examines acute lower respiratory infections (ALRI). Moreover, the research investigates hospital admissions related to respiratory diseases and cardiovascular disease (CVD) includes stroke. Additionally, the study explores chronic bronchitis incidence in adults, prevalence of bronchitis in children, incidence of asthma symptoms in children with asthma, and mortality from all causes among adults aged 30 years and older. And also, this study investigates the meteorological factors influence on distribution of air quality with the lockdown impacts. Existing research mainly focuses on air pollution distribution in Kolkata but often overlooks health risk studies. There is also a lack of data analysis on the spatiotemporal distribution of air quality and its relationship with weather conditions during different lockdown phases in urban and industrial areas. So, this study aims to fill these gaps by extending the focus beyond Kolkata to include four different districts, we seek to provide a more holistic understanding of air quality trends in the state. This approach aims to contribute valuable insights into the intricate relationship between air quality, meteorological factors and public health in diverse regions of West Bengal.

2 Materials and methods

2.1 Data collection and study area

The air quality data were collected from online monitoring stations operated by the Central Pollution Control Board (CPCB), India. The initial study locations—Asansol, Kolkata, Howrah, Haldia, Durgapur and Siliguri were chosen based on the accessibility of the online monitoring stations data (Fig. S1). Regrettably, Haldia and Durgapur had to be excluded from this analysis due to the absence of data for the relevant study periods. The CPCB online portal (https://app.cpcbccr.com/ccr/#/caaqm-dashboard-all/caaqm-landing) was used to collect daily data of seven air pollutants, including particulate matter (PM2.5 and PM10), sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), oxides of nitrogen (NOx), and ozone (O3, 8 hourly). The trend was analysed for the levels of PM2.5, PM10, NOX and O3 at different sites in 2019, 2020 and 2021. Based on the six criteria pollutants by CPCB, Air Quality Index (AQI) was calculated by the AQI calculator (https://app.cpcbccr.com/ccr_docs/AQI%20-Calculator.xls) provided by CPCB during 2019, 2020 and 2021 (monthly and yearly). The meteorological data such as temperature (Temp, ºC), relative humidity (RH, %), wind speed (WS, m/s) and wind direction (WD, degree) were collected from CPCB online portal and precipitation data was collected from NASA POWER Project's Data Access Viewer (DAV) (https://power.larc.nasa.gov/data-access-viewer/). Across the study period, the highest temperature was observed in Asansol in 2019, reaching 30.92 ± 5.82 ºC, while the lowest temperature was recorded in Siliguri at 23.30 ± 3.77 ºC in 2020. Relative humidity (RH) peaked in Kolkata in 2021 at 80.95 ± 12.55% and Asansol experiencing the lowest RH in 2019 at 70.42 ± 13.26%. Wind speed (WS) was highest in Howrah (2.57 ± 1.01 m/s) in 2021, whereas the lowest WS was noted in Kolkata in 2020 at 0.66 ± 0.24 m/s. Siliguri experienced the highest amount of rainfall compared to other areas over the study period. The annual average of meteorological variations in the study area is shown in Table S1.

2.2 Analysis of long- and short-term health effects of PM10 and PM2.5

In this study, AirQ + (v2.1), a programme developed by the World Health Organization (https://www.who.int/europe/tools-and-toolkits/airq---software-tool-for-health-risk-assessment-of-air-pollution), is used to determine both the long-term and short-term impacts of particulate matter on human health. The risk evaluation process in this programme uses concentration response functions with total populations and incidence per lakh populations [19]. This study analyses the long- and short-term effects (LTEs and STEs) of PM2.5 and PM10 on current concentration. Annual PM2.5 and PM10 concentrations, demographic information [29], and base line incidence rates per lakh population were used to evaluate LTEs and STEs of the study area [30]. The populations of Kolkata, Howrah, Asansol and Siliguri were about 4.50, 4.85, 1.24 and 0.71 million respectively. Table S2 represent the baseline incidence rate per 100,000 populations and the relative risk (RR) values. The software gave the cases' relative risk (RR) values. The AirQ + software provides the estimated number of attributable cases (ENACs) when all the data have been entered. In the current study, the following health effects are considered: mortality due to lung cancer (LC), ischemic heart disease (IHD), stroke and chronic obstructive pulmonary disease (COPD) in adults, acute lower respiratory infections (ALRI) in children aged 0–5 years, hospital admission from respiratory diseases and cardiovascular disease (CVD) (including stroke), chronic bronchitis incidence in adults, bronchitis prevalence in children, incidence of asthma symptoms in children with asthma, and mortality from all causes (adult age 30 years and older).

2.3 Statistical analysis

In this study, MS-Excel and SPSS v26 were utilized for the statistical analysis to ensure accurate and reliable results. Descriptive statistics were conducted using MS-Excel which providing a comprehensive overview of the data. Analysis of Variance (ANOVA) and partial correlation studies were performed with SPSS to identify significant differences and relationships among variables. Partial correlation offers significant advantages over Pearson's correlation, particularly in complex studies involving multiple interacting variables. Pearson's correlation measures the linear relationship between two variables without accounting for other influences. The partial correlation controls for the effect of one or more confounding variables, isolating the direct relationship between the variables of interest. This approach is particularly useful in multivariate contexts to understand the unique contribution of each variable and providing a clearer picture of the direct effects. For this study, partial correlation is used to better understand the effects of lockdown on air pollution distribution, where meteorological variables are considered. This method ensures a more reliable and precise analysis, highlighting the true impact of lockdown measures on air pollution. To enhance the visual representation of the study area and facilitate better understanding a detailed map was generated using ArcGIS version 10.3. Additionally, data cleaning, curing and outlier removal were meticulously carried out using MS-Excel to ensure the integrity and quality of the data.

3 Results

The spatial distribution of air pollutants in several cities of West Bengal, as presented in Table S3, demonstrate fluctuating patterns during the years 2019 to 2021 (Fig. 1). In general, the levels of PM10 and PM2.5 experienced a decline from 2019 to 2020, but showed a minor spike in 2021. Howrah consistently experienced the highest concentrations of PM10 (µg/m3) and PM2.5 (µg/m3), indicating substantial particulate matter emissions. The levels of NOx (µg/m3) and NO2 (µg/m3) exhibited a comparable trend of decrease from 2019 to 2020, followed by a subsequent rise in 2021. Kolkata and Howrah consistently recorded elevated levels of air pollutants as compared to Asansol and Siliguri. The concentration of O3 (µg/m3) has consistently risen in Kolkata and Asansol from 2019 to 2021, where in Howrah and Siliguri shows different pattern. On the other hand, the levels of SO2 have varied without a clear pattern. Conversely, carbon monoxide (CO, mg/m3) levels consistently shown a decline. Among the four districts, Howrah consistently had the highest concentrations of particulate matter (PM10 and PM2.5) throughout the study period (2019–2021). Oxide of nitrogen (NOx) levels were higher observed in Kolkata in 2019 as compared to 2020, in 2020 the levels of NOx were lower due to nationwide lockdown. As a capital city, Kolkata faces huge traffic, which raises the levels of nitrogen oxides in the ambient air. In 2019, the levels of O3 in Kolkata, Howrah, Asansol and Siliguri were found to be 29.24 µg/m3, 29.37 µg/m3, 20.63 µg/m3 and 29.40 µg/m3, in 2020 the O3 levels were 34.89 µgm3, 32.65 µg/m3, 21.58 µg/m3 and 24.25 µg/m3, and in 2021 the O3 levels were observed as 37.16 µg/m3, 25.55 µg/m3, 43.26 µg/m3 and 29.04 µg/m3 respectively. During the study period (from 2019 to 2021), Howrah had greater SO2 levels than other cities, while the CO levels were higher in Howrah in 2019 and 2021 and in 2020 the higher CO observed in Kolkata. In 2019, Howrah ranked highest in pollution levels for PM10, PM2.5, SO2, and CO among the four evaluated cities. Siliguri ranked highest in O3 levels, with Howrah, Kolkata, and Asansol following in decreasing order. Among the four cities investigated, Kolkata has the highest levels of pollution for nitrogen oxides (NOx) and nitrogen dioxide (NO2). Kolkata reached the highest rankings regarding both NOx and NO2 concentrations, with Howrah, Asansol, and Siliguri following closely after. In 2020 (lockdown year), the rank wise pollution load in four cities were: Howrah > Asansol > Kolkata > Siliguri for PM10; Siliguri > Howrah > Asansol > Kolkata for PM2.5; Siliguri > Howrah > Kolkata > Asansol for NOx; Howrah > Siliguri > Kolkata > Asansol for NO2; Kolkata > Howrah > Siliguri > Asansol for O3; Howrah > Kolkata > Asansol > Siliguri for SO2; and Siliguri > Kolkata > Howrah > Asansol for CO respectively. Similarly, in 2021 the rank wise four cities PM10, PM2.5, NOx, NO2, O3, SO2 and CO, were: Howrah > Asansol > Kolkata > Siliguri; Howrah > Asansol > Siliguri > Kolkata, Siliguri > Kolkata > Asansol > Howrah, Siliguri > Asansol > Kolkata > Howrah, Asansol > Kolkata > Siliguri > Howrah, Howrah > Kolkata > Asansol > Siliguri, and Howrah > Siliguri > Kolkata = Asansol. The pollutants levels in lockdown year 2020 decreases except O3. Furthermore, during the lockdown year, the levels of O3 in three cities, namely Howrah (32.65 µg/m3), Kolkata (34.89 µg/m3) and Asansol (21.58 µg/m3) were observed to be enhanced, whilst in Siliguri (24.25 µg/m3) it was found to be slightly dropped. The ANOVA analysis revealed statistically significant (p < 0.05) variations in pollution levels across different cities and years.

Fig. 1
figure 1

Spatial and temporal distribution of air pollutants in studied areas of West Bengal during the years of 2019 to 2021

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The levels of PM10, PM2.5, NOx, and O3 and their trend lines for the years 2019, 2020 and 2021 are depicted in Figs. 2, 3. With the exception of O3, the analysed sites' PM10, PM2.5 and NO2 levels violated the national ambient air quality standard (NAAQS) during January to March and October to December. The contaminant range is shown below the NAAQS levels for the months of June to September (rainy season). In 2020, the nationwide lockdown phase 1 (first lockdown) observed a sharp decline in pollutants except for O3 (increase) compared to 2019 and 2021. In 2021, West Bengal government implemented the 2nd phase lockdown (May 16 to May 30), showing the decline in pollutants level, however, a significant reduction observed in the 1st phase lockdown than the 2nd phase lockdown (Figs. 2, 3). Siliguri was the exceptional case where the levels of O3 declined in 2020 lockdown phase as compared to other cities. The NOx reduction observed very high in lockdown phase 1 as compared to phase 2. Rainfall is a great climatic characteristic for dramatically lowering the pollutant load in the atmosphere, as seen in Fig. 4 relationship between pollutants and precipitation, from June to September is the time when there is significant amount of precipitation occurs. The pollution levels in the Indian atmosphere during this time remain below the NAAQS levels due to monsoonal effects.

Fig. 2
figure 2

Trends of PM10 & PM2.5 during 2019, 2020 and 2021 where vertical green dotted line represents lockdown phase 1 period (2020), square box depicted lockdown phase 2 period (2021) and red line represents NAAQS. Y-axis represent concentration in µg/m3 and red line represent NAAQS levels

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Fig. 3
figure 3

Trends of NOx and O3 during 2019, 2020 and 2021 where vertical green dotted line represents lockdown phase 1 period (2020), square box is lockdown phase 2 period (2021) and red line represents NAAQS. Y-axis represent concentration in µg/m3 and red horizontal line represent NAAQS level

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Fig. 4
figure 4

Month wise distribution of air pollutants with rainfall

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The air quality index (AQI) calendar expresses the air quality in each month of the year during 2019 to 2021 (Fig. 5). One can observe that in Howrah and Kolkata, AQI was very poor during January 2019 (AQI: 344 and 363) and 2021(AQI: 307 and 306), whereas Asansol reported poor AQI in January month of 2019 to 2021 (AQI: 262 221 and 293). Siliguri AQI was very poor (309, 308 and 309) in January, February and March 2021. Kolkata, Howrah, Asansol and Siliguri AQI was poor in January 2020 and in 2019 and 2020 recorded poor AQI in January (AQI: 225 and 265). During January, February, March, November and December months, the AQI vary moderate to very poor over the cities. Whereas the average AQI during 2020 was good as compared to 2019 and 2021. In 2020, the lockdown’s effects work to lower the AQI in four cities, with substantially higher improvements in Kolkata and Siliguri. In the month of May to September the AQI was satisfactory to good during the study period. The yearly AQI in four cities were observed moderate in 2019 and 2021. During 2020, Kolkata and Siliguri AQI was good whereas Howrah and Asansol AQI were observed to be moderate but lower bound. The AQI varies significantly (p < 0.05) across different cities and months and years.

Fig. 5
figure 5

Monthly and yearly calendar of air quality index (AQI) from January 2019 to December 2021

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3.1 Correlation between air pollutants and meteorological factors

Based on the monthly average data of Kolkata, Howrah, Asansol, and Siliguri, partial correlation (Table 1) analysis was done to explore the relationship between air pollutant levels with meteorological conditions and lockdown. The results suggest that the levels of most of the air pollutants were negatively correlated with lockdown, precipitation, relative humidity, wind velocity and temperature. For the whole year, the levels of the seven pollutants were significantly negatively connected with rainfall and temperature (p < 0.05) with lockdown held constant. The partial-correlation coefficients between rainfall and PM10, PM2.5, O3, NO2, NOx, SO2, and CO concentration with lockdown held constant were − 0.475, − 0.439, − 0.364, − 0.314, − 0.326, − 0.337, and − 0.220, respectively, which means that the increase in rainfall will decrease the concentration of air pollutants. When the rainfall is kept constant, annual temperature and lockdown were significantly negatively (p < 0.05) correlated with the concentrations of most air pollutants, except ozone, which was significantly positively correlated with lockdown. The results of the multiple regression (Table S4) analysis revealed that PM, SO2, NO2 and NOx have significantly negative correlation (p < 0.05) with lockdown, temperature, wind speed, and relative humidity, and a significantly positive correlation was found for ozone with lockdown, however no significant change was found with temperature. The seasonal variation shows that the temperature is lower in winter season (20.64 °C) and higher in summer (30.06 °C). In the monsoon season, the temperature was 28.84 °C. The relative humidity follows a different pattern, being 69.57% in winter, increasing to 75.06% in summer and peaking at 88.72% during the monsoon. Wind speed is lowest observed in winter at 0.94 m/s, increases to 1.45 m/s in summer and is highest at 1.49 m/s in the monsoon. Similarly, rainfall is minimal in winter at 15.56 mm, rises substantially to 264.13 mm in summer and reaches its highest level at 446.63 mm during the monsoon.

Table 1 Correlations between various air pollutants and meteorological parameters
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3.2 Long- and short-term health risk assessments of PM10 and PM2.5

The estimated attributable number of cases (EANCs) of various health impacts as a result of PM2.5 and PM10 exposure over the long and short term are summarized in Tables 2, 3, 4 and 5 for Kolkata, Howrah, Asansol and Siliguri, respectively. The EANCs of mortalities all natural cases (adult age 30 + years) due to PM2.5 long term exposure in Kolkata, Howrah, Asansol, and Siliguri were 10,212, 13,829, 1423, and 1255, respectively. Similarly, PM2.5 short-term effects (mortality all causes in adult age 30 +) of four study areas were 1490, 2342, 223 and 193. For long-term PM2.5 exposer mortality due to chronic obstructive pulmonary disease (COPD) in adults, stroke, lung cancer (LC), ischemic heart disease (IHD) and acute lower respiratory infections (ALRI) in children aged 0–5 years in Kolkata were 974, 3969, 1132, 4381, and 571 respectively. The hospital admissions due to respiratory diseases in exposure to short-term effects of PM2.5 were 2828, 4427, 423 and 366 respectively in Kolkata, Howrah, Asansol and Siliguri, and hospital admission due to CVD were attributable 111, 174, 17 and 14 respectively. The higher risk of long term PM2.5 such as COPD, LC and IHD were observed in Howrah after that Kolkata, Asansol and Siliguri. The LTE and STE of PM10 such as chronic bronchitis in adults, post-neonatal infant mortality in all causes, bronchitis prevalence in children and occurrence of asthma symptoms in asthmatic children’s attributable cases were 30,860, 738,450, 1617 and 730 in Kolkata, 53,961, 898,920, 1920 and 890 in Howrah, 3782, 89,460, 197 and 88 in Asansol, and in Siliguri were 3293, 76,331, 170 and 75 respectively. The comparable similar studies in India and other countries findings are summarised in Table S5. Tables 1, 2, 3, and 4 (for Kolkata, Howrah, Asansol and Siliguri) illustrate the targeted long- and short-term health consequences linked to cases of PM2.5 and PM10, including mortality from stroke, IHD, LC, and COPD in adults, as well as ALRI in children aged 0 to 5 years. Hospital admissions resulting from respiratory illnesses, CVD (including stroke), prevalence of bronchitis in children, post-neonatal infant mortality from all causes, and incidence of asthma symptoms in asthmatic children with asthma are included in this analysis.

Table 2 The estimations of LTE and STE of PM2.5 and PM10 concentration on human heath in Kolkata
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Table 3 The estimations of LTE and STE of PM2.5 and PM10 concentration on human heath in Howrah
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Table 4 The estimations of LTE and STE of PM2.5 and PM10 concentration on human heath in Asansol
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Table 5 The estimations of LTE and STE of PM2.5 and PM10 concentration on human heath in Siliguri
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4 Discussions

Annual PM10 and PM2.5 levels in Kolkata, Howrah, Asansol, and Siliguri exceeded the NAAQS in 2019, 2020, and 2021. However, in 2020, the levels dropped compared to 2019 and 2021 due to nationwide lockdown effects [31,32,33,34]. In 2019, the annual nitrogen dioxide (NO2) level in Kolkata exceeded the NAAQ standard. This phenomenon was mainly due to a high volume of traffic, which was greater than that of other cities in the area [19]. The substantial traffic congestion in Kolkata has played a crucial role in causing high levels of NO2, hence emphasizing the distinct air quality difficulties faced by the metropolis in comparison to other similar urban areas [17]. This finding highlights the significant impact of vehicle emissions on air quality and emphasizes the necessity for special efforts to reduce traffic-related pollution in Kolkata. The annual NO2 levels were below the NAAQS in 2019, 2020 and 2021 in Howrah, Asansol and Siliguri, and concentration wise Howrah occupied the 2nd position after Kolkata. The levels of O3, SO2 and CO in 2019, 2020 and 2021 were below NAAQS over the year. The trend-line shows that the PM10, PM2.5 and NOx level show downward trend in 2020 in lockdown phase where only O3 concentrations increased [35,36,37]. The explanation lies in the basic ground level ozone formation chemistry [17, 38]. The community multi-scale Air Quality model analysis by Wang et al., (2021) revealed a noticeable increase in atmospheric oxidation capacity (AOC), which suggests that the higher levels of oxidants (up to + 25%) during the lockdown period were caused by an increase in O3 [37]. On the other side, the rise in non-methane hydrocarbons/oxides of nitrogen (NMHC/NOx) ratios during the social distancing tactics may be responsible for the rise in ozone concentrations. Although the NMHC/NOx ratios were higher when air masses came from industrial locations, the increase was also greater because these air masses were high in aromatic compounds and greatly boosted the reactivity of VOC [39].

Siliguri recorded the highest rainfall district compared to the other three. Rainfall therefore has the potential to influence the AQI. If we look at the air quality index, the monsoon season's AQI was better than other times of the year [40, 41]. Therefore, we can directly link the improvement of AQI to the contribution of washout. Low rainfall, temperatures and wind speed all contribute to the high AQI during the winter, which makes the pollutant locally stationary and unable to disperse over great distances [40, 42]. The high air pollution loads are typically observed in winter compared to summer and monsoon seasons [19]. In winter, lower temperatures and atmospheric inversions trapping pollutants close to the ground. In winter, higher consumption of heating fuels also contributes to higher emissions [20]. Additionally, lower wind speeds in winter reduce the dispersion of pollutants. Conversely, in summer and monsoon seasons, higher temperatures, increased wind speeds and frequent rainfall enhance the dispersion and removal of pollutants from the atmosphere. Rainfall, in particular, plays a crucial role in washing out airborne pollutants, resulting in significantly lower pollution levels during the monsoon season. Thus, meteorological conditions during winter exacerbate air pollution while those in summer and monsoon promote cleaner air. The partial correlation analysis examined the relationship between air pollutant levels, meteorological conditions and lockdown effects. The analysis revealed that most air pollutants were negatively correlated with lockdown, precipitation, relative humidity, wind speed and temperature. Rainfall and temperature, in particular, showed significant negative correlations with pollutant levels (p < 0.05) when lockdown was considered. Specifically, partial correlation coefficients with rainfall indicating that increased rainfall significantly decreases the air pollutant concentrations. Siliguri which was situated in the northern part of state of West Bengal, experience heavy rainfall compared to the other parts of the state. During the rainy season, pollutants are washed out from atmosphere hence reduce the concentration of air pollutants.

With the exception of ozone, lockdowns play a significant role in reducing air pollutants in the years 2020 and 2021 due to restrict industrial and vehicular activity. The entire nation was put on lockdown, as a result all types of transportation, entertainment, commerce, education and other activities were prohibited [17, 43]. In order to reduce the spread of infection, the lockdown seeks to interrupt the chain of interpersonal interaction. According to several researchers, the ambient air quality of numerous cities in numerous countries improved throughout the Lockdown [17, 33, 34, 44,45,46]. In comparison to the pre-lockdown period in Wuhan, China, Lian et al. (2020), found that the average monthly air quality index (AQI, 33.9%), PM2.5 (36.9%) and NO2 (53.3%) decreased during the lockdown except for O3 (116.6%) which concentration increased [44]. Similar to this, Kumari and Toshniwal (2020) examined the global effects of COVID-19 on the air quality of 12 cities from 162 stations throughout the world and found a decrease in PM2.5 (20–34%), PM10 (24–47%), and NO2 (32–64%) concentrations and a rise in O3 concentration [45]. In all the European nations during lockdown, the NO2 concentration decreased (30–50%) [47]. Two sites in Morocco, NO2 levels decreased (by 96%) throughout the lockdown period (March to April, 2020), according to ground-based observations by Otmani et al., [48]. Ali et al., studied on air pollution status during pre-lockdown, lockdown and post-lockdown in Kolkata and found that the average levels of PM2.5, PM10, SO2, NOx, and CO have decreased by about 73%, 72%, 48%, 84% and 61% respectively due to lockdown effects [43]. On the other hand, Kara et al. (2022) found interesting observations in Turkey during the COVID-19 pandemic. They observed that PM₁₀ and SO₂ levels increased by an average of 2.08% and 7.56% during curfew periods and by 6.15% and 32.01% during the normalization period, while NO₂ levels decreased by 5.19% during curfew and 1.92% during normalization compared to the previous year [49].The health risk assessment of long- and short-term exposer to particulate matter mainly PM10 and PM2.5 was done in Kolkata, Howrah, Asansol and Siliguri by using AIRQ + software. The long-term exposer to PM2.5, the health consequences such as mortality due to ischemic heart disease (IHD), stroke, lung cancer (LC) and chronic obstructive pulmonary disease (COPD) in adults and acute lower respiratory infections (ALRI) in children aged 0–5 years. The health risk attributable was observed higher ENACs in Howrah followed by Kolkata, Asansol and Siliguri respectively. The higher health risk shows in Howrah may cause of higher PM load and populations as compared to others. The second highest was Kolkata, Asansol and Siliguri have small population size as compared to Howrah and Kolkata. Similar study was done by Kamboj and Mathur (2022) in Kota Metropolis, Rajasthan [30]. In comparison, the stroke for adults attributable ENACs of long-term PM2.5 exposure was higher observed in Howrah and lower in Kolkata as compared to Kota. But compared with NCT Delhi and Alwar, the mortality due to stroke attributable in Howrah, Kolkata, Asansol and Siliguri showed lower ENACs. On the other side the long-term effects of PM10, the post-neonatal Infant mortality in all causes attributable ENACs shows higher in Howrah, Kolkata, Asansol and Siliguri as compared to NCT, Alwar and Kota in India [30, 50,51,52]. Among stroke, ischemic heart disease (IHD), lung cancer (LC), and chronic obstructive pulmonary disease (COPD), IHD is the leading cause of premature deaths in Kolkata, Howrah, Asansol, and Siliguri. This finding aligns with the previous studies [8, 19, 53]. This study provides crucial insights that can assist policymakers, stakeholders and governments in taking informed and targeted actions where they are most needed. By identifying ischemic heart disease (IHD) as the leading cause of premature deaths in key urban and industrial areas such as Kolkata, Howrah, Asansol, and Siliguri is highlights the urgent need for focused health interventions and policies aimed at reducing the burden of IHD. By pinpointing specific health challenges associated with air pollution, this study helps decision-makers with the necessary data to implement effective air quality management strategies, enhance healthcare infrastructure and promote public health initiatives. Targeted actions based on this study can lead to improved health outcomes, reduced healthcare costs and a better quality of life for the affected populations.

5 Conclusions

Overall, Howrah and Kolkata are more polluted than the other two study locations. In the urban areas Kolkata and Howrah, there were more particle pollution. The winter months witness greater PM concentrations, whereas the rainy months experience lower levels. In Kolkata, Howrah, Asansol and Siliguri, the average AQI varied from satisfactory to moderate values. The AQI revealed poor air quality in January, February, November and December than other months of the year. During the lockdown period of the study areas, the general air quality improved. The dispersion of contaminants in the atmosphere is significantly influenced by climatic variables mainly temperature wind speed, rainfall and others. The amount of ambient pollution in the atmosphere is significantly reduced by rainfall. The estimated attributable number of cases of various health impacts such as mortality due to stroke, ischemic heart disease, lung cancer, and chronic obstructive pulmonary disease in adults, all-causes mortality and acute lower respiratory infections in children aged 0–5 years as a result of PM2.5 and PM10 exposure over the long- and short-term effects are higher observed in Howrah and Kolkata as compared to Asansol and Siliguri. Compared to PM10, PM2.5 has a considerable negative impact on health. Because of the serious health consequences, particulate matter pollution is a crucial aspect that policymakers must take into account to maintain sustainability and address environmental concerns. Improved road systems, more green space, restrictions on open burning, banning of obsolete vehicles, use of public transportation and the conversion of diesel vehicles to clean energy vehicles can all reduce PM contamination.