Evaluating background and local contributions and identifying traffic-related pollutant hotspots: insights from Google Air View mobile monitoring in Dublin, Ireland

Mobile monitoring provides high-resolution observation on temporal and spatial scales compared to traditional fixed-site measurement. This study demonstrates the use of high spatio-temporal resolution of air pollution data collected by Google Air View vehicles to identify hotspots and assess compliance with WHO Air Quality Guidelines (AQGs) in Dublin City. The mobile monitoring was conducted during weekdays, typically from 7:00 to 19:00, between 6 May 2021 and 6 May 2022. One-second data were aggregated to 377,113 8 s road segments, and 8 s rolling medians were aggregated to hourly and daily levels for further analysis. We assessed the temporal variability of fine particulate matter (PM2.5), nitrogen monoxide (NO), nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), and carbon dioxide (CO2) concentrations at hyperlocal levels. The average daytime median concentrations of NO2 (28.4 ± 15.7 µg/m3) and PM2.5 (7.6 ± 4.7 µg/m3) exceeded the WHO twenty-four hours (24 h) Air Quality Guidelines in 49.4% and 9% of the 1-year sampling time, respectively. For the diurnal variation of measured pollutants, the morning (8:00) and early evening (18:00) showed higher concentrations for NO2 and PM2.5, mostly happening in the winter season, while the afternoon is the least polluted time except for O3. The low-percentile approach along with 1-h and daytime minima method allowed for decomposing pollutant time series into the background and local contributions. Background contributions for NO2 and PM2.5 changed along with the seasonal variation. Local contributions for PM2.5 changed slightly; however, NO2 showed significant diurnal and seasonal variability related to traffic emissions. Short-lived event enhancement (1 min to 1 h) accounts for 36.0–40.6% and 20.8–42.2% of the total concentration for NO2 and PM2.5. The highly polluted days account for 56.3% of total NO2, highlighting local traffic is the dominant contributor to short-term NO2 concentrations. The longer-lived events (> 8 h) enhancement accounts for 25% of the monitored concentrations. Additionally, conducting optimal hotspot analysis enables mapping the spatial distribution of “hot” spots for PM2.5 and NO2 on highly polluted days. Overall, this investigation suggests both background and local emissions contribute to PM2.5 and NO2 pollution in urban areas and emphasize the urgent need for mitigating NO2 from traffic pollution in Dublin. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-024-34903-5.


Introduction
Air pollution is recognized as a public health emergency for humans.Multiple epidemiological studies have been conducted globally, with evidence showing adverse health effects and premature death linked to short-term and longterm exposure to particulate matter and gas pollutants (Chen andKan 2008, Nuvolone et al. 2018;Samoli et al. 2001, World Health Organization 2004, 2013, 2016).Consistent research findings were shown from studies that were conducted in Ireland.Since the implementation of the Air Pollution Act in the 1990s, Ireland has experienced a decline in ambient concentrations of particulate matter, nitrogen dioxide (NO 2 ), and carbon monoxide (CO) (Carthy et al. 2020;Clancy et al. 2002), and the health benefits of air pollution controls showing reduced respiratory and cardiovascular deaths in Dublin.However, more recent research evidence highlighted there was no safe level of air pollutants to cause adverse effects.Quintyne et al. (2020) found the linkage of poor air quality with a rise in hospital admissions for asthma, chronic obstructive airway disease, and heart failure in Dublin city.Specifically, associations of outdoor particulate matter exposure with premature death from type 2 diabetes and cardio-respiratory diseases have been reported (McVicar et al. 2023;White et al. 2020).For example, Carthy et al. (2020) highlighted the associations between NO 2 pollution and asthma in older adults in Ireland.All stoke admission risks in Dublin were associated with increased PM 2.5 and NO 2 pollution during the winter season (Byrne et al. 2020), suggesting the exceedance of these pollutants in the short term could lead to acute health problems.The persistent efforts to mitigate air pollution, even at relatively lower levels, will continue to yield health benefits for the public.
In September 2021, the World Health Organization (WHO) launched stringent Air Quality Guidelines (AQGs) for major air pollutants, including inhalable particulate matter (PM 10 , with particle size equal to or less than 10 μm), fine particulate matter (PM 2.5 , with particle size equal to or less than 2.5 μm), NO 2 , ozone (O 3 ), sulfur dioxide, and CO to provide better protection for the general public (WHO 2021).Nearly, the whole global population (99%) breathes fine particles (PM 2.5 ) that exceed the new WHO AQGs annual level (5 µg/m 3 ) (Yu et al. 2023).European Environment Agency (2022) estimated that 96% of the urban population in Europe was exposed to PM 2.5 above the AQGs in 2020.As part of the European Union (EU) Green Deal, the EU Commission proposed to revise the Directive on Ambient Air Quality to align with the recently updated WHO AQGs (Tankosić 2023).To adhere to the legislation and EU directives, it is critical to maintain engagement in evidence-based policymaking, effectively reducing air pollution for the benefit of public health in Ireland.
Air quality characterization at a global or regional scale was mostly assessed using remote sensing (Brown et al. 2021).These methods have also been employed at the national or local level, for example, Kumari et al. (2022) employed satellites and ground-based data to characterize the air quality during the COVID lockdown in Dublin.Additionally, statistical models (e.g., linear or non-linear) using a small number of fixed site measurements in combination with geographic and temporal parameters have been developed to predict air pollutants at the city level (Alam andMcNabola 2015, Basu et al. 2019;Donnelly et al. 2019).These studies are limited by a need for high-resolution data.Notably, most epidemiological studies regarding air pollution and health effects are generally based on ambient concentrations measured from limited representative regulatory stations or derived from modeling results (McDuffie et al. 2021).In complement to conventional methods, mobile monitoring proves to be an effective approach to assessing the temporal and spatial variabilities of air pollutants at a fine scale.
Google Street View vehicles equipped with air sensors have been employed for comprehensive traffic air pollution investigation in North America (e.g., Denver, San Diego, Oakland) and European cities (e.g., Amsterdam, Copenhagen, London) (Alexeeff et al. 2018;Apte et al. 2017;Kerckhoffs et al. 2022;Whitehill et al. 2020).Along with the rapid economic growth, urbanization, and increasing number of private cars (Tubridy et al. 2022), people in Dublin experience bad traffic congestion similar to that of other European citizens.Road traffic emission represents a major source of PM 2.5 , CO, and NO 2 , with nitrogen oxides (NOx) serving as one of the main precursors of O 3 formation.Severe traffic congestion could lead to toxic levels of traffic-related air pollutants (Jin and Jin 2023), and there was evidence indicating that health damage could be attributed to diesel vehicles in Dublin, Ireland (Dey et al. 2018).Ireland's national and local air quality monitoring networks are essential to ensure compliance with air quality standards.However, limited studies provided a microscale level assessment of traffic-related air pollutants.Responding to this data gap, high-resolution monitoring of gas (CO, NO 2 , O 3 , nitrogen monoxide (NO), carbon dioxide (CO 2 )) and particulate pollutants (e.g., PM 2.5 ) was performed using Google's electric Street View car at street level in Dublin city, Ireland.
Using high-resolution criteria pollutants mapping allows a comprehensive understanding of the spatiotemporal variation of air pollution within Dublin city, thereby enabling the formulation of mitigation policy targeting localized air pollution phenomena.The objectives of this paper are to (1) assess the daytime trend, weekday variations, and seasonal patterns of criteria air pollutants; (2) assess the temporal variations of local and background contribution to time serial air pollution; and (3) address the spatial complexities of hyperlocal air quality.This meta-data enables real-time information about the exposure level and hotspots for further air quality management and public awareness.Furthermore, it is critical for a growing urgency for mitigation strategies and future environmental policy to meet the zero-pollution action plan for 2030 and the zero-pollution version for 2050.

Study area
Dublin is the capital of the Republic of Ireland, with a population of 1.45 million in 2022 (Central Statistics Office 2022) and an approximate area of 118 km 2 .The Dublin city center is characterized by a mix of residential and commercial buildings.A study indicated that about 10.5% and 9.8% of Ireland's national CO 2 and PM 2.5 emissions from traffic sources occurred in Dublin city (Alam et al. 2018).It has a transport network with national roads (N) and regional roads (R), tram (i.e., LUAS), train (i.e., DART), and cycle lanes.Dublin has a maritime climate without significant temperature extremes and is characterized by mild-warm summer (12-16 °C) and cool winter (4-8 °C) (McHugh et al. 2023).The prevailing wind direction in Ireland is from the southwest (Broderick et al. 2006).

Measurement protocol and mobile air sensing platform
A hyperlocal air pollutants monitoring campaign using an all-electric Street View vehicle (Jaguar I-PACE) equipped with Aclima's mobile air measurement and data acquisition platform was set up in Dublin, Ireland, over 16 months.This Google Street View car was characterized by zero tailpipe emission.The Aclima's mobile air sensing platform was driven through roads and streets in Dublin, following fixed routes and time slots to capture air pollutants at a high spatiotemporal resolution.In general, mobile monitoring was repeatedly conducted in Dublin city on weekdays from Monday to Friday during daytime hours, between 7:00 and 17:00, with a limited number of monitoring sessions extending until 18:00 and 19:00.A Global Positioning System (GPS) was used to record the geographical location, which was synthesized with the air monitoring platform for date and time.Figure 1 shows the mobile monitoring routes.
This study focused on mobile monitoring data collected from May 2021 to May 2022.The metadata platform provides information about data collection, data management, data processing methods, measurement calibration, quality control and quality assurance (QA/QC) protocol, and uncertainty quantification.QA/QC procedures for the Google Air View project in Dublin and data processing protocols/ algorithms in this study followed the reports of Apte et al. (2017) andWhitehill et al. (2020).Instrument performance including precision, bias, and drift for the monitored pollutants (NO, NO 2 , O 3 , CO, CO 2 , PM 2.5 ) is shown in Table S1.

Ambient monitoring data
Hourly PM 2.5 , NO 2 , and O 3 concentrations measured from urban traffic, urban background, and suburban across Dublin were obtained from the national and local air quality networks (https:// airqu ality.ie/).The ambient air quality data collected during the mobile monitoring period were incorporated for comparison.Information about the ambient sites can be referred to Table S2 of the Supporting Information (SI).

Data cleaning and data aggregation
We performed a series of data processing and data aggregation steps to convert data into different time resolutions.Initially, concentrations from one-second (1 s) measurements (except O 3 ) that fall below the limit of detection (LOD) were substituted with half the LOD of the monitored air quality features.The 8 s rolling median was aggregated from 1 s monitoring during the "drive pass" time window (Apte et al. 2017).Any 8 s rolling median below the LOD will be substituted with the corresponding 1/2 LOD value.The LODs for the monitored pollutants at 1 s and 8 s resolutions are listed in Table S1 of the SI.The statistical summary of the raw data for the 1 s and 8 s rolling median can be seen in Table S3 of the SI.The final dataset consists of 5.03 million 1 s measurements of PM 2.5 , NO 2 , NO, CO, and CO 2 , and 1.45 million of O 3 .Furthermore, we excluded any missing values for the key variables (e.g., 23.8-38.6%)(Table S3b) and retained 377,113 8 s measurements.The data was subsequently down-sampled to 1-h and daytime resolution to reduce the size of the dataset.

Statistical analysis
In this study, PM 2.5 concentrations are reported in the unit of µg/m 3 .NO 2 and NO recorded in parts per billion volumes are converted to NOx, which is expressed as NO 2 in the unit of µg/m 3 in this study.We employed the Kolmogorov-Smirnov test to check central tendency and data distribution, and the median values were reported as core estimates.One-hour and daytime median values were derived from 8 s median aggregation.Instead of the daytime average, a daily 1-h maximum of O 3 concentration was utilized in air quality evaluation.Correspondingly, subsampled data (e.g., high pollution days) was produced based on the 1-h and daytime resolution that showed concentrations exceeded the WHO AQGs and EU legal standards to explore the pollution episodes and hotspots.Statistical results were expressed in terms of interquartile range, including 1st, 5th, 25th, 75th, 95th, and 99th percentile.Data processing and statistical analysis were performed using the R statistical computing environment (https:// www.r-proje ct.org/), and ggplot2 R package was used for data visualization.

Baseline extraction
Atmospheric background and accumulated local emissions contribute to time series variation of air pollutant concentrations in urban area (Li et al. 2019b).In mobile monitoring, the background signal (e.g., low-frequency variations) is characterized by smooth variation at an hourly scale, while the rapid concentration spikes occur from seconds to minutes (Tan et al. 2014).In this study, we employed a baseline extraction method to divide the monitored concentrations (C t (k, j) ) into the background and local emission concentra- tions using the following equations: where C lc (k, j) refers to local emission concentrations for pollutant k at time j, and C bg (k, j) represents the background concentration for pollutant k at time j.The contribution from local emissions pollution (P lc (k, j) ) is the ratio of C lc (k, j)∕C t (k, j).
The hypothesis proposes that the lowest percentile ( C t,i (k, j) ) signifies a background concentration character- ized by a stable variation, which is minimally influenced by peaks originating from local sources (e.g., on-road pollution).The same method has been employed in previous studies to divide on-road background and accumulated vehicle emissions of the mobile monitoring air concentrations (Bukowiecki et al. 2002;Li et al. 2019b;Wei et al. 2021).In this study, we employed the 5th percentile for a "drive pass" time window (i.e., 8 s) to reconstruct a complete baseline to account for the background contribution to PM 2.5 and NO 2 .Additionally, minima of 1-h median and daytime median were employed for the estimation of short-lived and longerlived local pollution events.

Optimized hotspot analysis
The identified highly polluted days that exceeded the WHO AQGs for 24-h levels of NO 2 and PM 2.5 were employed as input (Table 3) for optimized hotspot analysis.Both pooled data and single-day monitoring were included for analysis.In this study, the significant hot (i.e., significant spatial clusters of high values) and cold spots (i.e., low values) were calculated using the Getis-Ord Gi* statistics in Arc GIS Pro.We employed "count incidents within fishnet grid" method for aggregating datasets, while PM 2.5 and NO 2 concentrations were spatially joined in the analysis field.This approach has been utilized for hotspot analysis in prior research (Cummings et al. 2021).The tool aggregates the measured pollutants (e.g., points) and performs scale of analysis, and the outputs were corrected for both spatial dependence and multiple calculations.The outputs included a new feature class with a z-score, p-value, and significance level categories (Gi_Bin: 99% hot, 95% hot, 90% hot, not significant, 90% cold, 95% cold, and 99% cold).

Summary statistics of monitored air pollutants
The current meta-data covers a total of 377,113 singlestreet segment (8 s) monitoring across Dublin.Table 1 shows descriptive statistics of mobile monitoring for PM 2.5 , NO 2 , O 3 , CO, and CO 2 .Specifically, the median NO 2 concentration was 27.0 µg/m 3 across single-segment measurements, and 95th of NO 2 concentration reached 280 µg/m 3 .The median PM 2.5 concentration was 6.6 µg/m 3 , with the 95th reaching 19.3 µg/m 3 .The median and 95th of measured CO 2 were 442.3 ppm and 495.9 ppm.Median O 3 and CO concentrations across the segments were 46.2 µg/m 3 and 0.316 ppm, respectively, with 95th reaching 77.8 µg/m 3 and 0.697 ppm.The mobile platform enables measurements with a high spatial coverage of traffic-related air pollutants at the hyperlocal level.The findings revealed that particle and gas pollutant concentrations exhibit significant dynamics across road segments.Previous studies indicated that spatiotemporal variability of air pollutants through mobile monitoring would be affected by various factors, including background matrix, meteorology, road traffic (e.g., speed limits, fleet composition), street topology, or pollution of spikes originating from nearby sources (Hofman et al. 2022a;Tang et al. 2020).
As shown in Table 2, this mobile monitoring campaign is characterized by a high coverage density and measuring frequency for 1491 h on 233 sampling days.The 1-h median for NO 2 varies from 14.2 to 470.4 µg/m 3 .The results in Table 2 showed that the EU legislation for 1-h NO 2 limited values (i.e., 200 µg/m 3 ) exceeded 6 h/year (0.4%).Some evidence suggests short-term exposure to a 1-h mean value of NO 2 below 200 µg/m 3 is associated with adverse health effects for patients with severe asthma (Latza et al. 2009).The daytime median of NO 2 was 24.8 µg/m 3 , and 115 (49.4%) of the sampling days exceeded the 24-h WHO AQGs for NO 2 (25 µg/ m 3 ) during the monitoring period.During daytime monitoring, PM 2.5 concentrations vary from 1.4 to 29.5 µg/m 3 , with a median of 6.3 µg/m 3 .The results showed that the targeted WHO AQGs for 24 h PM 2.5 (15 µg/m 3 ) were exceeded in 21 days/year (9%).The WHO recommended to implement corrective actions to mitigate against PM 2.5 pollution if the 24-h mean exceeded 3-4 days/year.The dominance of NO 2 is reflected in the numbers of hours and days that pollutant concentrations exceeded the hourly and daily recommended concentrations.Tubridy et al. ( 2022) highlighted a breach of the EU limit value for NO 2 and PM 2.5 was evident in Dublin.These results were consistent with the findings from the Environmental Protection Agency (EPA), Ireland, which reported that NO 2 is the primary pollutant from the transport sector in Dublin (Spohn et al. 2021), followed by PM 2.5 and PM 10 (Tang et al. 2019).As shown in Table 2, the AQG levels (range from 100 to 120 µg/m 3 ) did not exceed for 8-h mean O 3 concentrations.The findings from the mobile monitoring, which recorded the average 8-h O 3 concentration (48.0 µg/m 3 ), align with previous results observed in Dublin at the urban site.McHugh et al. (2023) indicated that O 3 concentrations in Dublin at urban sites were consistently lower than those in suburban or coastal areas.The WHO has no AQGs set for CO 2 and concentrations of CO remained below the AQGs throughout the monitoring period.

Time series of air pollutants
Figure 2 shows the temporal variability of PM 2.5 , CO, NO 2 , CO 2 , and O 3 between May 2021 and May 2022.In addition to hourly and daytime variations, the 5-day moving average and data smoothing across the monitoring period are illustrated in Fig. 2. The smoothing pattern shows the PM 2.5 concentration exceeded the WHO AQGs for an annual value of 5 µg/m 3 .Furthermore, the smoothing trend highlights a rise in concentrations observed during the winter season.The monthly variations of the monitored pollutants are presented in Fig. S1.PM 2.5 concentrations were lower from May to August, experiencing a steady increase in December and January.These findings align with results obtained from fixed-site monitoring at community sites in Dublin.For example, Lee et al. (2022) found that the ambient PM 2.5 concentrations (9-13 µg/m 3 ) in Dublin surpassed the WHO AQGs with significant seasonal variability showing higher PM 2.5 levels in December, which could be attributed to solid fuel burning for domestic heating in the winter season.
As can be seen in Fig. 2, the measured NO 2 is highly dynamic during the on-road mobile monitoring.The 5-day moving average shows a higher frequency of elevated NO 2 pollution events compared to PM 2.5 .The smoothing pattern demonstrated the NO 2 concentrations were above the WHO AQGs for an annual value of 10 µg/m 3 .Much greater monthly variation is evident for NO 2 than PM 2.5 (Fig. S1).There was moderate evidence showing that short-term exposure to a 24-h mean NO 2 value of ~ 50 µg/m 3 increased respiratory hospital admission and mortality (Quintyne et al. 2021).The CO and CO 2 concentrations fluctuate slightly across seasons.In general, the 5-day moving average shows similar peaks and valleys occurred across measured pollutants except O 3 .For the 8-h average O 3 , an inverse trend was shown compared to NO 2 , with the maximum concentrations The potential relationships between the mobile monitored pollutants were investigated using Spearman's correlation coefficients, and results are shown at temporal variations from 1-h and daytime levels in Fig. S2.Weak correlations were found for 1-h and daytime median PM 2.5 with CO 2 (r s = 0.26), CO (r s = 0.25), and NO 2 (r s = 0.27-0.30),most likely due to the varied sources of PM 2.5 emission in Dublin.A report from EPA Ireland indicated major sources of ultrafine particles in Dublin were secondary pollutants and oil, traffic, solid fuels, and cooking (Ovadnevaite et al. 2021).
Moderate to strong correlations were found for NO 2 with CO (r s = 0.48-0.67)and CO 2 (r s = 0.47-0.74),suggesting that they share similar emissions from the transport sector (Ghahramani and Pilla 2021).In contrast, negative correlations were shown for O 3 with CO 2 , CO, and NO 2 .This is expected since O 3 is a secondary pollutant, with NO 2 serving as its precursor.There was no notable weekday variability observed for the monitored pollutants (Fig. S3).In contrast, other studies have reported varying results, such as Martínez Torres et al. (2020) found that NO 2 concentrations were higher from Wednesday to Friday compared to other weekdays in Dublin.Weekday-weekend variation is not the primary focus of this study and has been discussed in previous articles.For example, Lee et al. (2022) suggested that weekday PM 2.5 concentrations were higher than weekends in Dublin.

Diurnal variation of air pollutants
Figure 3 illustrates the boxplots of hourly variation of the mobile monitored PM 2.5 , NO 2 , O 3 , CO, and CO 2 , respectively, during daytime (mainly from 7:00 to 17:00) between May 2021 to May 2022.One-hour medians are utilized to characterize the diurnal variation.In terms of temporal evolution, although mobile monitoring was conducted from different postal areas and across various seasons in Dublin, the basic trend illustrates elevated levels of PM 2.5 in the early morning (8:00-9:00) and in the early evening around 17:00, respectively, during weekdays (Fig. 3). Figure S4 shows the diurnal variation in average PM 2.5 from ambient locations characterized by urban background, suburban background, and urban traffic.Peak ambient PM 2.5 values occurred at 20:00-21:00, showing higher night-time concentrations than daytime.These elevated levels are most likely attributed to regional transportation emission from residential solid combustion.These findings align with the results from air pollution monitoring conducted outside three maternity hospitals in Dublin.The study reported a similar daytime pattern for PM 2.5 and showed higher night-time PM 2.5 concentrations than daytime levels (Lee et al. 2022).Additionally, the results indicated these high concentration events for PM 2.5 occurred mainly during working hours on weekdays in December.
Hourly averages of NO 2 exhibited a distinct daytime variation trend, with peaks observed at 7:00-8:00 in the morning and 17:00-18:00 in the early evening.Martínez Torres et al. (2020) found a similar NO 2 diurnal pattern in Dublin, showing the highest NO 2 concentration at 8:00-9:00 and 17:00-18:00.These findings indicate that attribution of the direct impact of traffic emission on NO 2 is more evident than for PM 2.5 , suggesting NO 2 serves a better indicator for characterizing traffic-related air pollution in both short-term and long-term.These patterns coincided with the morning rush hour and traffic hour after work, and the valley values were observed at noon.A previous study revealed that the average hourly traffic achieved the peak value during the morning (8:00 am) and afternoon traffic (4:00-5:00 pm) in Dublin city (Tang et al. 2019).Moreover, the time and distribution pattern of the peaks shifts with the seasons, with the morning peaks in June occurring at 7:00-8:00.Evening peaks for NO 2 concentrations mostly occurred at 16:00 in December and January.Previous findings in the UK suggested a distinct shift in peak timing to earlier evenings in the winter season (Wyche et al. 2020).Figure S3 shows that ambient NO 2 levels gradually increased from 5:00 am, and the morning peaks occurred at about 8:00 in the morning at the urban traffic site and 9:00 at the urban and suburban background sites.The evening peaks occurred at 18:00 at the traffic site and shifted to 19:00-21:00 at the urban and suburban background sites.These results highlight the impact of non-traffic emissions.
O 3 exhibited a contrasting diurnal trend compared to PM 2.5 and NO 2 , and the highest O 3 concentrations were observed in the afternoon (13:00-15:00).This could be due to photochemical effects that increase O 3 concentrations during the daytime.The diurnal pattern for O 3 is consistent with previous findings from ambient sites in Dublin (Tripathi et al. 2012) and modeling results (Donnelly et al. 2019).The O 3 concentrations from mobile monitoring exhibited consistently lower concentrations than other suburban or background areas.A previous study was conducted in Dublin to investigate the spatial-temporal variation of traffic and urban monitoring sites (Perillo et al. 2022), and the results demonstrated reductions in PM 2.5 and NO 2 concentrations and an increase in O 3 concentrations during COVID restrictions.Diesel vehicles are prominent sources of NO 2 and PM 2.5 (Tang et al. 2019).The CO diurnal profiles showed a similar but less clear pattern than NO 2 , which reflects the diesel-powered vehicle fleet.As for CO 2 , higher concentrations were mostly shown in the early morning.

Partitioning pollution into the background and local contribution
The total pollutants time series from mobile monitoring were divided into background and locally emitted contributions.In this study, we chose the 5th percentile of 1 h (Table S5), minima over 1-min, 1-h, and daytime data as background at different time resolutions.Compared to the 5th percentile of the whole dataset, the corresponding averages of minima for 1-h NO 2 deviated by about 5%, while they doubled for PM 2.5 .The development of the 5th percentile curve for PM 2.5 and NO 2 shows a consistent background trend compared to the minima during the entire monitoring period (Fig. S5).Similarly, strong correlations were shown for PM 2.5 from the estimated background (1-h minimum) and those measured at the urban background site (r s = 0.76, p < 0.01).Based on these results, the uses of minima are considered valid to obtain an estimate of the local contribution at different time resolutions where mobile monitoring was conducted.Furthermore, as suggested by Zimmerman et al. (2020) and Hofman et al. (2022b), a change on the order of 1 h is considered a short-lived event, and a longerlived event is defined as occurring on a time scale of daytime basis (e.g., 2-8 h).
Figure 4 shows the division of total pollution into background contributions alongside short-lived and longer-lived events for PM 2.5 and NO 2 .Of the total enhancement above background, the short-lived event enhancement (1 min to 1 h) accounts for 20.8-42.2% and 36.0-40.6% of the total concentration for PM 2.5 (Fig. 4a) and NO 2 (Fig. 4b).For short-lived events, NO 2 exhibits sharper peaks than PM 2.5 .These results indicate the general public is at potentially higher risk during evening rush hours when background and local traffic emissions are tremendous.As can be seen in Fig. 4c and d, the longer-lived events (i.e., daytime) account for about 25% of the total concentration for both pollutants.Furthermore, previous findings suggested there are currently severe issues associated with air pollution in Dublin arising from both the transport sector and the use of solid fuel for domestic heating (Ovadnevaite et al. 2021).As for NO 2 , The decomposition results for the highly polluted days with the background and local contributions are illustrated in Fig. S6.Of particular interest are the pollution episodes that occurred on 17th January and 29 March 2022, revealing decomposed background concentration of NO 2 and PM 2.5 reaching 53.6-77.8µg/m 3 and 13.0-20.4µg/m 3 , which were mainly due to background contribution, could be regional Fig. 4 Time series decomposition of background and local contribution for PM 2.5 and NO 2 .Notes: a and b refer to short-lived events for PM 2.5 and NO 2 (i.e., 1 h), and c and d refer to longer-lived events for PM 2.5 and NO 2 (i.e., ~ 8 h) transported sources.During these regional pollution events, high concentrations of NO 2 and PM 2.5 were observed at urban and suburban background ambient sites.Subsequently, we compared the diurnal variability of PM 2.5 and NO 2 to illustrate the temporal trend and potential contributions from local and background sources during highly polluted days.As shown in Fig. 5, for short-lived events, traffic emissions contribute to both PM 2.5 and NO 2 .The results show the diurnal trends are evident in NO 2 concentration in the short term.The mean NO 2 concentration during traffic peak time is a factor of 2 to the background and a factor of 2-3 to the off-peak.These results are consistent with previous findings from Zimmerman et al. ( 2020), which suggested near road concentrations of PM 2.5 were only moderately elevated, while NO 2 has much higher near roadway concentration enhanced (> 3-5 times).The probability of experiencing adverse health responses could be greatest when the concentration of NO 2 is elevated.During polluted days, local NO 2 has two peaks associated with morning (8:00) and early evening (16:00-18:00) rush hour, contributing to 54.4-68.0% of total NO 2 concentrations.This means that the monitored NO 2 is significantly enriched mainly from local traffic emissions.Additionally, there were peaks around 17:00-18:00 for background PM 2.5 and NO 2 ; this may suggest that their temporal variations are influenced by nontraffic sources.Overall, NO 2 had the larger temporal variations (33.2%), whereas PM 2.5 was less variable (22.3%).This is consistent with the traffic site monitoring results as shown in Fig. S3.Song et al. (2021) suggested diesel vehicles have larger NOx emission factors than gasoline vehicles.These findings highlighted the influence of diesel traffic on elevated NO 2 levels in Dublin.
Figure S7 enables a comparison of the time series variability of PM 2.5 and NO 2 from mobile monitoring and ambient air quality monitoring stations (AQMS) on the same timescale.The time series variability of ambient and mobile results exhibited similarities but showed significant differences in concentrations.For example, mobile results were significantly higher than the traffic monitoring site for PM 2.5 (7.2 µg/m 3 , p < 0.01), but less for NO 2 (41.1 µg/m 3 ) (Table S3).A total of 20.2% of the monitoring days showed higher mobile NO 2 concentration than the corresponding traffic site measurement.As expected, PM 2.5 is spatially homogeneously distributed across the city, and higher associations were found for mobile monitoring with AQMS measurement of PM 2.5 (r s = 0.72-0.80)than for NO 2 (r s = 0.33-0.37)(Table S4).Carthy et al. (2020) found that the Dublin traffic zone had the highest NO 2 levels compared to other areas.A study by Li et al. (2019a) highlights that mobile measurement represents the urban stationary monitors within a 1 km 2 area.These findings indicated that mobile monitoring exhibited a high variability compared to air quality monitoring stations, emphasizing the need for extensive spatial coverage to capture the variability of NO 2 from traffic emission.

Hotspots of traffic-related air pollutants
By ranking the most polluted days along with sampling routes at the community level from high to low, Table 3 shows the details of several cases in which daytime NO 2 and PM 2.5 exceeded the WHO guidelines of 25 µg/m 3 and 15 µg/m 3 throughout the monitoring period.Mapping of the elevated NO 2 and PM 2.5 concentrations for these days is displayed in Fig. 6, and pollution hotspots were identified for PM 2.5 and NO 2 .Table 3 outlines the hotspots description for PM 2.5 corresponding to optimal hotspots.For example, high values of PM 2.5 cluster are observed at highly trafficked areas in Dublin, notably along National Roads such as N1 at O'Connell Street Upper, N2 near Grangegorman Upper, N3 at Dalymount, and N4 at Saint John's Road West.In addition, Regional Roads like R101 at Cork Street and R109 from Parkgate to Chesterfield venue were PM 2.5 hotspots with 90 to 99% confidence.High concentrations of PM 2.5 clusters are also linked to industrial activities, for example, vehicle traffic at the landside of Dublin Port (Djordjević et al. 2023), and emissions from nearby restaurants.Our findings in Fig. 6 further indicated that hotspots of NO 2 are more dispersedly distributed than those of PM 2.5 .To compare the results for pooled data, Fig. S8 shows consolidated information derived from single-day output.The location and timing of hotspots varied from day to day, and the recurrent hotspots in specific locations suggest that there are areas in Dublin where traffic-related pollutant concentrations are consistently elevated relative to the surrounding area.A recent publication indicates a signification association between urban green space and decreased NO 2 concentrations in Dublin (Sabedotti et al. 2023).Identification of hotspots for trafficrelated pollution provides potential policy instruments from an urban planning perspective to prioritize urban public transit, walking, and cycling networks within the city.Establishing motorized traffic-free zones and expanding public green spaces would also contribute to reducing the PM 2.5 and NO 2 levels.

Limitation of the study
While our study provided comprehensive insights into Google Air metadata, it is limited by the lack of weekend and nighttime monitoring of traffic-related air pollutants, which hinders a thorough assessment of diurnal variations and the analysis of meteorological effects on traffic-related pollution.The varied monitoring time of each segment could affect the spatial and temporal assessment of the measured pollutants.Our assessment of the decomposition of shortlived events did not consider the traffic fleet composition or street topology.There were no supporting data to explore the origin of pollution sources about the contribution from longer-lived events.Further research could explore the integration of regional pollution sources to develop targeted

Conclusions
In this study, we demonstrate the potential for a mobile monitoring approach to investigate the hyperlocal distribution of air pollutants in Dublin, Ireland.This investigation characterizes traffic-related gas and particle pollutants after the COVID pandemic and provides an illustration of temporal variability, hotspot characterization, and local and background concentration contributions for mobile monitored PM 2.5 and NO 2 .Median concentrations of daytime NO 2 and PM 2.5 exceeded the WHO 24-h AQGs for 49.4% and 9% of the sampling days throughout the monitoring period.The diurnal pattern was more evident for NO 2 than PM 2.5 , with peak values occurring at 8:00 and 17:00-18:00.Seasonally, PM 2.5 and NO 2 concentrations are higher in winter than in the summer season, while no clear weekday variations were shown.Our findings demonstrate that elevated trafficrelated NO 2 in Dublin exhibited significant diurnal and spatial variations.Detailed knowledge of background and local contribution to mobile monitoring is important for highly polluted regions.This meta-data can be further implied for the sensitive analysis of the representativeness of mobile measurements and to obtain robust modeling prediction in space and time.We can draw several conclusions by comparing the decomposed concentration and ambient data, NO 2 is more affected by traffic emissions.Our data highlight the importance of non-traffic sources for temporal variation in the early evening.While traffic is a dominant source of NO 2 , non-traffic sources appear to be the dominant source for PM 2.5 .The importance of non-traffic sources suggests that merely regulating vehicle emissions may be insufficient to reduce PM 2.5 concentrations to meet the new WHO guidelines.The findings highlight hyperlocal air monitoring is a complementary tool to provide meta-data for the research community.It enhances understanding of the implications of air quality policies and the effectiveness of air pollution mitigation plans from multiple emission sources.Furthermore, it enables providing air quality assessment to local environmental departments and policymakers for developing targeted strategies for mitigating traffic-related air pollution at a localized level.

Fig. 1
Fig. 1 Overview of the "Google Air View" mobile monitoring routes (orange dots) in Dublin city from May 2021 to May 2022.The blue dots indicated the location of the urban background, suburban background, and urban traffic sites

Fig. 2
Fig. 2 Time series of hourly a PM 2.5 , b CO, c NO 2 , d CO 2 , and e O 3 from Google Air View vehicle during May 2021 and May 2022 in Dublin city.Hourly median concentrations on each day were used for calculation, except the O 3 hourly maximum was utilized for calculation.Notes: The red dash lines show the 5-day moving average vari-

Fig. 3
Fig. 3 Diurnal trends (left) and heatmap (right) of a PM 2.5 , b NO 2 , c O 3 , d CO, and e CO 2 by Google Air View vehicle in Dublin city (n = 1486 h).Box plots represent the 25th and 75th percentiles, with

Fig. 5
Fig. 5 Diurnal variation in background and short-period emission of PM 2.5 and NO 2 during the entire study period.Notes: Lines represent shortlived events and areas represent background concentrations; the golden color refers to PM 2.5 and the blue color refers to NO 2

Fig. 6
Fig. 6 Spatial distribution of highly polluted a PM 2.5 and b NO 2 along with optimized hotspots analysis results for c PM 2.5 and d NO 2 in Dublin

Table 1
Data points for the 8 s rolling median from single segment drive pass d NOx in µg/m 3 is expressed as NO 2 .At 20 °C and 760 mmHg, a conversion factor was used, i.e., (NO ppb + NO2 ppb) * 1.9125 = NO x µg/m 3 c

Table 2
Summary descriptive statistics of hourly and daytime concentrations for PM 2.5 (µg/m 3 ), NO 2 (µg/m 3 ), O 3 (µg/m 3 ), CO (ppm), and CO 2 (ppm) from mobile monitoring Percentage was calculated using the exceeding hourly or days divided by total sampling hours or days c SD standard deviation d Daily refers to the daytime statistics in this study e Data points for the hourly and daytime statistics aggregated from 8 s median f Refers to WHO AQGs and EU standards, respectively g in April and May, while minimum values were shown in November and December.

Table 3
Description of identified highly polluted daytime median of PM 2.5 and NO 2 and elevated concentrations a Daytime median > 15 µg/m 3 for PM 2.5 and > 25 µg/m 3 for NO 2