Ambient Air Quality Data Research Articles

Evaluating the influence of sulphur dioxide and nitrogen dioxide in ambient air on biochemical factors contributing to plant tolerance.

Air pollutants, specifically, Sulphur Dioxide (SO2) and Nitrogen Dioxide (NO2) cause numerous biochemical changes in plants; by studying the biochemical parameters, plant resistance against these pollutants can be evaluated. The effect of air pollutants on biochemical parameters such as ascorbic acid (AA), total chlorophyll (TC), pH and the relative water content (RWC) of leaves has been investigated in the present study. The objective of the present study was to observe the relationship between biochemical parameters of plants and ambient air pollutants (SO2 and NO2). The morphological and biochemical changes of the sampled leaves of different plants were studied. Simultaneously, ambient air quality data was collected from the central pollution control board (CPCB) online portal. Among the sampled plants, Ficus religiosa and Morus alba plant species can be recommended as tolerant for the sampling sites: Jalandhar, Amritsar, and Ludhiana. Ficus benghalensis and Ficus religiosa can be recommended as tolerant for Chandigarh sector 25 and 53 respectively. As a result, it has been observed that SO2 has a negative effect on the relative water content and ascorbic acid content of most plant species, while NO2 can have ambiguous effect on plants (toxic and beneficial), and no strong association has been found with biochemical parameters. The current study provides pollutant-specific information and insights into the variations caused by air pollutants. The present study will contribute in suggesting the tolerant plant species that could provide new approaches for developing urban landscapes.

Impact of regional climate change on apple yield in Himachal Pradesh, India

Climate change has become a major global as well as regional challenge because of its adverse effects on biodiversity and agriculture. Himachal Pradesh, a state in India, is situated in the western Indian Himalayan region, which predominantly features an agricultural economy, with apple being the major cash crop of the region. This paper presents the trend analysis of climatic parameters to delineate the effect of climate change on the apple yield in the region from 1991 to 2018. The temperature and rainfall data were obtained from the Indian metrological department, while snowfall data and ambient air quality data were collected from the database of NASA. The chill unit was calculated using the UTAH model. The climatic parameters were compared with apple yield to identify the impact of climate change on the crop. Therefore, apple yield reduction risk was calculated. The trend analysis indicated a temperature decrease during the monsoon months and a temperature increase during winters. The same analysis also indicated decreased snowfall by 0.375 kg/m2 and increased rainfall during the spring season by 23.2 mm, 19.6 mm, 10.4 mm (between mid-February and mid-April from 1985 to 2020). The chill units and apple production showed a positive correlation. In summary, our results indicated that climate change has reduced the apple yield by 11.48%.

Assessment of the Ambient Air Quality of Sangli City, Maharashtra, vis-à-vis National Ambient Air Quality Standards (NAAQS)

Assessment of ambient air quality is important to formulate short-term and long-term plans to mitigate air pollution. This study attempts to present an ambient air quality assessment for Sangli City, located at 16.85° N, and 74.58° E. The assessment is done through ambient air quality data measured over three ambient air quality monitoring stations located in residential, commercial, and industrial zones of Sangli City. The data was collected from 2009 to 2017 under the National Air Quality Monitoring Program of the Central Pollution Control Board (CPCB), India. The observed concentrations of gaseous pollutants, SO2 and NOx, and respirable particulate matter are compared with the concentration levels prescribed by the National Ambient Air Quality Standards (NAAQS) in 2009. The concentration levels of particulate matter are quite on the higher side of the prescribed standards. The levels of NOx are almost approaching the prescribed values, whereas SO2 levels are well within the margin. We have also studied seasonal variations in the pollutants during the study period. In general, the air quality in Sangli City is quite undesirable during the mid-winter to mid-summer period. The study is useful for common men to increase awareness. It also forms baseline data and is useful for local and central government to design appropriate strategies to apply corrective measures, protect health, and promote environmental protection.

Spatial and Temporal Variations in Ozone Pollution and Sensitivity Characteristics in Hainan Island

Based on ambient air quality data, meteorological observation data, and satellite remote sensing data, the temporal and spatial variations in ozone (O3) pollution, the sensitivity of O3, and its relationship with meteorological factors in Hainan Island were analyzed in this study. The results showed that the maximum daily 8-h moving mean (O3-8h) in western and northern cities in Hainan Island was higher than that in the central, eastern, and southern cities. O3-8h was the highest in 2015, and O3-8h exceeding the standard proportion was the largest in 2019. In addition, O3-8h was positively correlated with average temperature (P<0.1), sunshine duration (P<0.01), total solar radiation (P<0.01), atmospheric pressure, and average wind speed and was negatively correlated with precipitation (P<0.05) and relative humidity. The satellite remote sensing data showed that the tropospheric NO2 column concentration (NO2-OMI) and HCHO column concentration (HCHO-OMI) displayed opposite trends in Hainan Island from 2015 to 2020. Compared with those in 2015, NO2-OMI increased by 7.74% and HCHO-OMI decreased by 10.2% in 2020. Moreover, Hainan Island belongs to the NOx control area, and the FNR value exhibited a fluctuating downward trend in the past 6 years, with a trend coefficient and climatic trend rate of -0.514 and -0.123 a-1, respectively. A strong correlation was observed between meteorological factors and the FNR value of Hainan Island.

Multidimensional Data Analysis of Ambient Air Quality Based on Apache Kylin

While the environmental monitoring department increases the number of monitoring points and the frequency of monitoring, it will also bring about a surge in the amount of monitoring data and computational response events. However, the traditional data statistics methods based on relational databases are ineffective in the face of huge environmental monitoring data. Aiming at ambient air quality data analysis, this paper uses Hadoop, Hive, Kylin, and other tools to build a multi-dimensional analysis platform for ambient air quality big data in a distributed environment, which realizes the unified storage, calculation, and analysis of ambient air quality monitoring data. Compared with the traditional relational database statistical analysis scheme, the proposed solution significantly improves the efficiency of statistical analysis of ambient air quality data under the condition of large data. The response time is shortened by 98%, reaching the sub-second level.

A Method to Predict CO2 Mass Concentration in Sheep Barns Based on the RF-PSO-LSTM Model.

In large-scale meat sheep farming, high CO2 concentrations in sheep sheds can lead to stress and harm the healthy growth of meat sheep, so a timely and accurate understanding of the trend of CO2 concentration and early regulation are essential to ensure the environmental safety of sheep sheds and the welfare of meat sheep. In order to accurately understand and regulate CO2 concentrations in sheep barns, we propose a prediction method based on the RF-PSO-LSTM model. The approach we propose has four main parts. First, to address the problems of data packet loss, distortion, singular values, and differences in the magnitude of the ambient air quality data collected from sheep sheds, we performed data preprocessing using mean smoothing, linear interpolation, and data normalization. Second, to address the problems of many types of ambient air quality parameters in sheep barns and possible redundancy or overlapping information, we used a random forests algorithm (RF) to screen and rank the features affecting CO2 mass concentration and selected the top four features (light intensity, air relative humidity, air temperature, and PM2.5 mass concentration) as the input of the model to eliminate redundant information among the variables. Then, to address the problem of manually debugging the hyperparameters of the long short-term memory model (LSTM), which is time consuming and labor intensive, as well as potentially subjective, we used a particle swarm optimization (PSO) algorithm to obtain the optimal combination of parameters, avoiding the disadvantages of selecting hyperparameters based on subjective experience. Finally, we trained the LSTM model using the optimized parameters obtained by the PSO algorithm to obtain the proposed model in this paper. The experimental results show that our proposed model has a root mean square error (RMSE) of 75.422 μg·m-3, a mean absolute error (MAE) of 51.839 μg·m-3, and a coefficient of determination (R2) of 0.992. The model prediction curve is close to the real curve and has a good prediction effect, which can be useful for the accurate prediction and regulation of CO2 concentration in sheep barns in large-scale meat sheep farming.

The use of fire radiative power observations to determine spontaneous combustion event activities associated with coal mining on the Mpumalanga Highveld

Coal mining is a significant activity on the Mpumalanga Highveld. One of the major air pollution issues associated with coal mining, is the spontaneous combustion of coal. There are no abatement technologies in place for such emissions, and typically long- and shortlived greenhouse gases, other gaseous pollutants and particulate matter are emitted by such events. For ambient air quality models to accurately capture the contribution of spontaneously combusted coal, it is necessary to determine the locations and durations of these burning events. Such information will also assist in explaining experimentally determined ambient air quality data. In this article, satellite fire radiative power (FRP) data from the Moderate Resolution Imaging Spectro-Radiometer (MODIS) was used to determine the locations and durations of spontaneously combusted coal within the Mpumalanga Highveld for January 2001 to December 2019. From the results it was concluded that five mining sites were prone to spontaneous combustion. These sites were all opencast mines situated on old bord and pillar mines. Two of these areas were actively burning for most of the 19-year study period. A relatively well-defined seasonal pattern was also observed, with combustion events being more prevalent during the winter months. Considering the active burning periods of the areas where spontaneous combustion were recorded, it is obvious that this is a major source of atmospheric pollutants on the Mpumalanga Highveld.

Potential impacts of Washington State's wildfire worker protection rule on construction workers.

Driven by climate change, wildfires are increasing in frequency, duration, and intensity across the Western United States. Outdoor workers are being exposed to increasing wildfire-related particulate matter and smoke. Recognizing this emerging risk, Washington adopted an emergency rule and is presently engaged in creating a permanent rule to protect outdoor workers from wildfire smoke exposure. While there are growing bodies of literature on the exposure to and health effects of wildfire smoke in the general public and wildland firefighters, there is a gap in knowledge about wildfire smoke exposure among outdoor workers generally and construction workers specifically—a large category of outdoor workers in Washington totaling 200,000 people. Several data sources were linked in this study—including state-collected employment data and national ambient air quality data—to gain insight into the risk of PM2.5 exposure among construction workers and evaluate the impacts of different air quality thresholds that would have triggered a new Washington emergency wildfire smoke rule aimed at protecting workers from high PM2.5 exposure. Results indicate the number of poor air quality days has increased in August and September in recent years. Over the last decade, these months with the greatest potential for particulate matter exposure coincided with an annual peak in construction employment that was typically 9.4–42.7% larger across Washington counties (one county was 75.8%). Lastly, the ‘encouraged’ threshold of the Washington emergency rule (20.5 μg m−3) would have resulted in 5.5 times more days subject to the wildfire rule on average across all Washington counties compared to its ‘required’ threshold (55.5 μg m−3), and in 2020, the rule could have created demand for 1.35 million N-95 filtering facepiece respirators among construction workers. These results have important implications for both employers and policy makers as rules are developed. The potential policy implications of wildfire smoke exposure, exposure control strategies, and data gaps that would improve understanding of construction worker exposure to wildfire smoke are also discussed.

Application of LSTM Models in Predicting Particulate Matter (PM2.5) Levels for Urban Area

Air pollution in India poses a big threat to human lives. In 2017, 77% of population of India was subjected to PM2.5 (Particulate Matter) exposure resulting in mortality of 6.7 lakh throughout the country. In this study, Long Short-Term Memory (LSTM) model, a powerful deep learning technique is applied for PM2.5 prediction. Three variants of LSTM model, LSTM for regression, LSTM for regression using window and LSTM for regression with time steps are developed to predict PM2.5 concentration in India. The metrics used to evaluate the performance of the predictive models are root mean square error (RMSE) and coefficient of determination (R2). The models are applied to continuous ambient air quality data collected from 14 stations in India, for the period from May 01, 2019 to April 30, 2020 at an interval of every 15 minutes. The optimal results are obtained from the models with the tuned parameters of 64 epochs and batch size of 32. All the three variants of LSTM model performed equally well in predicting PM2.5 concentration. The experimental results revealed that the value of R2 is maintained at 0.9 consistently for all the variants of LSTM model. The low values of RMSE and high values of R2 proved the reliability of the model. Thus, the proposed model gives awareness about the air pollution level in India and alerts the society to take precautionary steps to save their lives. Further the urban planners can have idea of the pollution levels for their planning and decision making.

A Comparative Study to assess the Air quality of Ludhiana, India amid COVID-19

Air quality Index (AQI) is a tool which is implemented to assess the qualitative and quantitative status of air pollutants. Four distinctive pollutants; PM10, PM2.5, SO2 and NO2 were used to compare the prevailing ambient air quality in the study region-Ludhiana, the industrial city in the north Indian state of Punjab. The AQI for Ludhiana has been compared and, the ambient air quality data for the same has been obtained for Punjab Agricultural University, Ludhiana from Central Pollution Control Board (CPCB). The data has been compared for fifteen days before and after the lockdown amid COVID-19. Air quality information uncovers that during lockdown period PM10 and PM2.5 levels were decreased by around 35 to 40%, which might be clarified as conceivable decrease from production sector, transportation, and residue. AQI of Ludhiana revealed that the primary reason for the poor air quality is P.M 10 and P.M 2.5 which significantly reduced to around 10 to 35 percent during pandemic lockdown.

Understanding seasonal variation in ambient air quality and its relationship with crop residue burning activities in an agrarian state of India.

In India, Indo-Gangetic Plains (IGP) is becoming the hotspot of air pollution due to increasing anthropogenic activities such as rapid industrial growth, infrastructure development, transportation activities, and seasonal practice of crop residue burning. In the current study, seasonal variation in ambient air quality for 14 parameters, i.e., particulate matter (PM), trace gases, and volatile organic compounds (VOCs), along with meteorological parameters, was studied in 21 districts of the Haryana state for year 2019, situated in IGP. To analyze spatial variation of pollutants, ambient air quality data of 23 continuous ambient air quality monitoring stations were divided into three zones based on ecology and cropping pattern. All the zones showed annual mean PM10 and PM2.5 concentrations much higher than national ambient air quality standards. Annual mean PM10 concentration (±standard deviation) in Zones-1, 2, and 3 was 156±86, 174±93, and 143±74 μg m-3, whereas for PM2.5 was 71±44, 85±54, and 78±47 μg m-3. The results showed a considerable seasonal variation in the concentration of all pollutants. Most of the pollutants peak in the post-monsoon season, followed by winters in which crop residue burning predominates in many parts of the Haryana. PM10 concentrations increased by 65-112% and PM2.5 concentrations increased by 131-147% in the post-monsoon season compared to monsoons. The post-monsoon season showed the highest concentration of PM10, NO, and toluene (Zone-1); and PM2.5, NH3, CO, and benzene (Zone-2); whereas in winters, SO2 (Zone-1); ethylbenzene, m,p-xylene, and xylene (Zone-2); and NO2 and NOx (Zone-3) showed the maximum pollution levels. The O3 concentration was highest in the pre-monsoon season (Zone-1). The satellite-based fire counts and PCA results show a significant influence of crop residue burning in the post-monsoon season and solid biomass burning in winters on Haryana's air quality. The study could help to understand seasonal variation in ambient air quality and the influence of factors such as crop residue burning in the IGP region, which could help to formulate season-specific control measures to improve regional air quality.

An Investigative Study on Perceived Indoor Air Quality During COVID-19 Lockdown in India

The air we breathe is a worldwide concern, especially in developing countries like India. Recently, the coronavirus pandemic resulted in restricted activities which showed noteworthy improvement in ambient air. As it hassled people to reach home, corporations began work from home schemes. Conforming to WHO, coronavirus is airborne as transmission cannot be disregarded in crowded and inadequately ventilated indoor spaces. A high-density population spending more time indoors raises questions on existing indoor requirements of accommodating a healthy long-duration inhabitation. Indoor time is also likely to extend with higher than before acceptance to online learning, entertainment, and shopping. Even though air pollution is mostly thought of as an outdoor concern, studies suggest that the air indoors can be contaminated as well, and sometimes even more than the air outside. India has shown growth in building-related illness, whereas lack of specific standards and limited research raises a concern. The countrywide lockdown to control the coronavirus situation in India has brought overwhelming improvement in the air quality due to restricted anthropogenic activities. This study is based on a pan India survey and ambient air quality data by the central pollution control board to understand the change in outdoor air pollution and indoor air quality perception before and during the lockdown in 205 cities categorized as per government guidelines. It also aims to understand the relationship between various parameters and perceived indoor air quality. Even though the COVID-19 pandemic has bought unprecedented health and economic crisis, the pandemic may act as a promoter for healthy and sustainable indoor environments.

WITHDRAWN: The impact of industry cluster and environmental policies on residents' health risk evaluation using big data.

Ahead of Print article withdrawn by publisher.

Compilation and Evaluation of Ambient Respirable Crystalline Silica Air Quality Data near Sand Quarries and Processing Facilities

Ambient respirable crystalline silica air quality is of concern to many communities near mineral processing facilities and to regulatory agencies serving these communities. Accurate air quality data are needed to compare measured respirable crystalline silica concentrations at the fencelines of mineral processing facilities with the published health effect guideline published by the California Office of Health Hazard Assessment (OEHHA). This article is a compilation and evaluation of air quality studies around a diverse set of nineteen sand producing facilities. The respirable crystalline silica air quality data compiled by Air Control Techniques, P.C. and most of the data compiled by other researchers cited in this article have been measured using EPA Reference Method samplers adjusted for respirable crystalline silica sampling and NIOSH Method 7500 X-ray diffraction analyses. The authors conclude that (1) the ambient concentrations in the diverse set of mineral processing facilities were consistently lower than the 3.0 microgram per cubic meter chronic reference exposure level (REL) adopted by OEHHA, (2) upwind-to-downwind fenceline concentration differences were small, and (3) the fenceline t concentrations were often at background concentration levels. The authors recommend additional sampling studies to better characterize background concentrations of ambient respirable crystalline silica.

The relationship between air pollutants and maternal socioeconomic factors on preterm birth in California urban counties

BackgroundPreterm birth is the leading cause of perinatal morbidity and mortality in the U.S. and disparities among racial and ethnic groups persist. While etiologies of preterm birth have not been fully elucidated, it is probable that environmental and social factors play a role.ObjectiveWe hypothesized that there is an interactive association between exposure to fine particulate matter (PM2.5) or ozone (O3) and neighborhood socioeconomic factors that increase the risk of preterm birth.MethodsWe conducted a retrospective study using geocoded birth certificate data between 2007 and 2011, daily ambient air quality data on PM2.5 and O3, and American Community Survey (2007–2011 5-year estimates) data to assess census tract-level socioeconomic factors in California urban counties.ResultsOur study found a small positive association between maternal exposures to PM2.5 and O3 and preterm birth that varied by gestational exposure period. In mixed-effects models, we found an increase in the risk of preterm birth for a one-unit change in PM2.5 averaged across the entire pregnancy (AOR = 1.02, 95% CI: 1.01, 1.02) and O3 during 3-months pre-pregnancy (AOR = 1.03, 95% CI: 1.02, 1.04). Interaction between census tract-level factors and air pollutants showed an increase in the risk of preterm birth among mothers living in higher socioeconomic areas, though, a fixed cohort bias sensitivity analysis showed these associations were not significant.SignificanceThese findings substantiate previous studies that showed associations between air pollution and preterm birth, even as pollution levels have decreased. This study has important implications for policy decisions and may help inform research on potential mechanisms of preterm birth.

Ambient air quality data reported at Sasol Secunda monitoring stations during COVID-19 lockdown – Mpumalanga, South Africa

Various local and international research has been published on the effects of COVID-19 lockdown on ambient air quality. In most cases, a reduction in ambient NOx and PM concentrations have been observed with varying changes in ambient SO2 levels. Secunda, located in the Highveld Priority Area in Mpumalanga, South Africa is known for its large industrial facilities utilising coal as primary feedstock. The towns of Secunda and eMbalenhle provide the majority of the workforce to Sasol and has therefore been the focus of this study. The ambient air quality in the Secunda region was assessed due to the changes in human behaviour during lockdown, familiarity with the Sasol facility and the strategic locations of ambient air quality stations.Results show a clear decrease in ambient CO, NO2 and PM concentrations, especially during the first two weeks of lockdown. Only subtle changes were observed for ambient H2S and SO2 pollutant concentrations at the ambient monitoring stations. An increasing trend in all ambient species was observed towards the end and post lockdown, in contrast to declining ambient temperatures with the onset of winter. This is also contrary to the reduction in emissions from the factory that conducted annual maintenance in the month following lockdown (phase shutdown). This article concludes that human behaviour has a material local ambient impact on CO, NO2 and PM pollutant species, while H2S concentration profiles are more directly related to the industrial complex’s levels of activity. Ambient SO2 trends did not show a similar correlation with the facility’s activities (as H2S), but a stronger correlation was observed with the diverse local and regional sources in close proximity to Secunda and eMbalenhle. The influence of better dispersion especially on a local scale, brought about by more effective emission heights, is considered material. Moreover, meteorological factors, on local air quality, has been shown to be a material contributor to observed ambient air quality levels in the study domain

Time Series Analysis of Air Quality in Chennai city

A prime issue faced in India is the quality of air. As per World Health Organization (WHO) report, 2.4 million people die each year as a result of Air Pollution globally. In this study, the Air Quality Index (AQI) is analyzed based on the database monitored at different monitoring centres in Chennai in India. For this purpose, a data set consisting of monthly Ambient Air Quality data of Chennai city with various air pollutants for commercial, residential and industrial area were collected from Central Pollution Board, Chennai. This research article aims to understand air pollution aspects in the Chennai city by identifying an appropriate time series model and the result were analyzed.

Ambient air quality and the risk for Chronic Obstructive Pulmonary Disease among Metro Manila Development Authority traffic enforcers in Metro Manila: An exploratory study

BackgroundAir pollution and poor ambient air quality are significantly related to multiple health risks. One associated disease is chronic obstructive pulmonary disease (COPD), a preventable disease with several contributing factors and one of the leading causes of morbidity/mortality locally and globally. A potentially high-risk population are traffic enforcers who are constantly exposed to air pollution. In the Philippines, the MMDA has the widest coverage in traffic management. The study determined the risk of COPD among Metro Manila Development Authority (MMDA) traffic enforcers in relation to ambient air quality level, as well as identified other factors that increase the risk of developing COPD.MethodsFifty-two MMDA traffic enforcers deployed in PM2.5 air quality sensor areas in Metro Manila from 2016 to 2018 were recruited through stratified sampling. The International Primary Airways Guidelines (IPAG) questionnaire was utilized to measure risk of COPD. Respiratory health and working history were obtained through questionnaires. Department of environment and natural resources provided PM2.5 ambient air quality data which aided in the construction of the Exposure-Month Index. Ordinal logistic regression was used to examine the association of PM2.5 together with the relevant factors and the risk of COPD.ResultsWe found statistically significant associations between PM2.5 and COPD among high risk category [odds risk (OR): 1.24, 95% confidence interval (CI): 1.07–1.44]. Age (Moderate, OR: 1.16, 95% CI: 0.98–1.38 and High, OR: 10.06, 95% CI: 4.02–25.17) and chest pain (Moderate, OR: 68.65, 95% CI: 1.71–2.75 × 103) were potential risk factors, whereas body mass index (BMI) (OR: 0.05, 95% CI: 0.01–0.53) exhibited protective effect.ConclusionsExposure to PM2.5 was associated with an increased risk of COPD among high-risk category MMDA traffic enforcers. Age and chest pain were potential risk factors to risk of COPD, whereas BMI exhibited a potential protective effect. Results of this study can be used for clinical management of high-risk populations, such that of MMDA traffic enforcers.

A Comparative Study on Air Pollution Characteristics in Four Key Cities during 2013 in Guangxi Province, China

Based on ambient air quality data of the four key cities (Nanning, Liuzhou, Guilin, and Beihai) in Guangxi, China, along with an analysis of the main emission sources, topographic features, weather conditions, and backward trajectories, the variation of main air pollutants and pollution episodes in the four cities were studied. Results showed that air pollution was most serious in Liuzhou and Guilin, followed by Nanning and Beihai. PM2.5 was the dominant pollutant in each city, followed by O3, PM10, and NO2. Concentrations of SO2 and CO did not exceed their National Ambient Air Quality Standard Grade II limit values. In the cities, the concentrations of PM2.5, PM10, NO2, and SO2 were high during fall and winter and low during spring and summer, while the concentrations of O3 were high during fall and low during the other seasons. Concentrations of CO were low during summer and high during the other seasons in Nanning and Liuzhou, while they were high during spring and winter and low during summer and fall in Guilin and Beihai. In these cities, pollution episodes resulted mainly from stagnant accumulation and showed characteristics of regional pollution. However, pollution levels and durations for each city were different due to differences in main pollution sources, local geography, and weather conditions. The influences of air masses on the four cities were similar. They were mainly influenced by local emission sources in the spring, while during autumn, long-distance transportation from Hunan and Hubei was significant. In winter, air pollution in Nanning and Beihai was mostly affected by local emission sources, and that in Liuzhou and Guilin was mainly affected by long-distance transportation from the south and northeast of Guangxi.

Air Pollution and Emergency Department Visits for Disease of the Genitourinary System.

The aim of this study is to determine associations between ambient air pollution and the number of emergency department (ED) visits for diseases of the genitourinary tract in Toronto, Canada. We used the National Ambulatory Care Reporting System (NACRS) database to obtain the related ED visits and developed statistical models using daily data on ED visits, temperature, relative humidity, and outdoor air pollution concentration levels. The NACRS database contains data on hospital-based and community-based ambulatory care. The environmental data were retrieved from the National Air Pollution Surveillance (NAPS) program. The NAPS is the main source of ambient air quality data in Canada. We considered 2 air quality health indexes and 6 air pollutants: daily means of fine particulate matter PM2.5, O3, CO, NO2, SO2, and also maximum 8-hour average ozone. For every air pollutant, we fit 270 models (15 lags × 18 strata). We found that same-day air pollution concentrations have the highest number of statistically significantly positive associations with ED visits for genitourinary health outcomes. A total of 133 positive associations were identified over the 14 days lag. In subgroup (strata) analysis, females older than 60 years of age were found to have the most positive associations. In particular, nitrogen dioxide was found to be highly associated with ED visits for females over 60; an increase in NO2 was associated with an increased relative risk (RR) of ED visits when lagged over 0, 1, and 2 days (RR = 1.040 [95% confidence interval: 1.028, 1.052], 1.020 [1.009, 1.032], and 1.025 [1.013, 1.036], respectively). The values of risks are reported for a 1 interquartile range increase in concentration (8.8 ppb). Our results suggest that urban ambient air pollution affect the number of ED visits due to genitourinary system conditions.