Modelling of Tropospheric Ozone Concentration in Urban Environment

Tropospheric ozone is harmful to human health and plants. It is resulted from photochemical processes involving NOx and VOCs from reactions of motor vehicle emissions and solar radiation in polluted urban environment. Historical data in Jakarta indicated that ozone concentrations often exceeded ambient standard threshold.  To minimize its impact to human health it is important to predict its concentration. This paper reports the use of multivariate statistical method to predict ozone concentration, using precursor concentration and meteorological parameters.  CH4, CO, NMHC, NO, NO2, THCdata concentration, wind direction and speed, temperature, solar radiation and relative humidity during 2011 - 2012 were used to build the model. Multiple linear regressions were applied to predict ozone concentration at Thamrin Station, Jakarta. These data were used as predictors at time (t) to estimate the ozone concentration at time (t +1). Meteorological conditions were found to strongly affect the concentration of ozone. The strongest relationship was found between ozone and temperature (0.513, p = 0.000). Weaker but significant positive correlations were found for  solar radiation and NO2 (r = 0.242, p= 0.000),. NMHC and NO correlation (r= 0.353, p= 0.000).  Both NO and NMHC are freshly emitted from exhaust gas.  Correlations between humidity, wind speed and direction were negative. Methana, NMHC, were negatively correlated with ozone due to their roles for producing NO2 as the main precursor, while NO was for its scavenging reaction with O3. Based on Adjusted R2 value, all predictors could explain variation in ozone concentration of approximately 46.32%. These findings will be useful as input in urban transportation planning and management in cities with tropical climate like Indonesia, as all precursors are emitted from vehicle combustion

Space-time exposure modelling of troposheric O3 in Europe

Exposure models need to be developed which can be applied at the continental scale, while still reflecting local variations in exposure conditions. Land use regression (LUR) has been widely adopted to describe the spatial variations in air pollutants over the longer term but not for short-term time-variable exposures. This study, therefore, aimed to develop and validate a space-time O3 model applicable to epidemiological studies investigating the health effects of short-term (e.g. daily) O3 exposures at the small-area scale. A geographical information system (GIS) was developed, incorporating data from 1211 O3 monitoring sites across Western Europe and a range of predictors, stored as 100m grids, including land cover, roads, topography and meteorology. The spatial model consisted of a LUR model representing the long-term average for years 2001-2007. The monitoring sites were classified, using multivariate statistical techniques, into 13 site types based on a set of descriptive indicators, then 13 temporal models represented by time functions were produced – one for each site type. These were linked to the spatial model using probability of group membership as a weighting factor. Finally, local meteorological data were incorporated to produce the full space-time model to predict daily concentrations for point locations. The spatial and temporal models were individually evaluated based on agreement with measurement data from a reserved subset of 20% of the monitoring sites. The performance of the spatial model was similar to other continental LUR models (R2=0.67; RMSE=7.64 μg/m3), while performance of the temporal models ranged from 0.3 to 0.5 (R2). Including local meteorological data into the full spatial-temporal model improved correlation with the concentrations measured at 30 monitoring sites in the Netherlands (R2= 0.42 without; R2=0.53 with meteorology). Modelling daily O3 over large areas at a fine spatial scale is possible using this approach. Overall model performance was further improved as the temporal period was aggregated to weekly or monthly. The model was applied to mothers in two birth cohorts in the European Study of Cohorts for Air Pollution Effects (ESCAPE) to provide daily O3 exposure estimates, which can be aggregated as needed to provide individualised exposures based on date of birth

The effect of short-term changes in air pollution on respiratory and cardiovascular morbidity in Nicosia, Cyprus.

Presented at the 6th International Conference on Urban Air Quality, Limassol, March, 2007. Short-paper was submitted for peer-review and appears in proceedings of the conference.This study investigates the effect of daily changes in levels of PM10 on the daily volume of respiratory and cardiovascular admissions in Nicosia, Cyprus during 1995-2004. After controlling for long- (year and month) and short-term (day of the week) patterns as well as the effect of weather in Generalized Additive Poisson models, some positive associations were observed with all-cause and cause-specific admissions. Risk of hospitalization increased stepwise across quartiles of days with increasing levels of PM10 by 1.3% (-0.3, 2.8), 4.9% (3.3, 6.6), 5.6% (3.9, 7.3) as compared to days with the lowest concentrations. For every 10μg/m3 increase in daily average PM10 concentration, there was a 1.2% (-0.1%, 2.4%) increase in cardiovascular admissions. With respects to respiratory admissions, an effect was observed only in the warm season with a 1.8% (-0.22, 3.85) increase in admissions per 10μg/m3 increase in PM10. The effect on respiratory admissions seemed to be much stronger in women and, surprisingly, restricted to people of adult age

Distribution and temporal behaviour of O3 and NO2 near selected schools in Seberang Perai, Pulau Pinang and Parit Buntar, Perak, Malaysia

Air quality has deteriorated in urban areas as a result of increased anthropogenic activities. Quantitative information on the influence of meteorological conditions on several pollutants in a tropical climate is still lacking. Real-time ozone (O3) and nitrogen dioxide (NO2) levels were measured nearby selected schools in Malaysia to examine the impact of meteorological factors on monitoring pollutants. The results showed the overall 10 min average concentrations of the main parameters during school holiday were 24 ppb (O3) and 33 ppb (NO2) while during school day the overall 10 min average concentrations were 26 ppb (O3) and 51 ppb (NO2). Although there are no minimum requirements for short-term exposure by MAAQG, if compared to 1 h average requirements, all concentrations were still below the suggested values. Regarding spatial distribution, a different trend in pollutant concentration among the schools was observed because of the influence of temperature (AT) and wind speed (WS). The results were verified by Pearson correlation, where signifi cant correlations (p<0.01) were determined between air pollutants and meteorological factors, which were temperature, wind speed and relative humidity. Meanwhile, the distribution of O3 was moderately correlated with NO2. However, the results of multivariate analysis indicate that temperature and relative humidity had the most significant influence on the formation of O3. In summary, the results of this study showed that all precursors and meteorological parameters contribute to the production of O3. Hence, reducing O3 precursors, which are emitted by vehicles, is essential to lessening the exposure to O3

Estimating exposure response functions using ambient pollution concentrations

This paper presents an approach to estimating the health effects of an environmental hazard. The approach is general in nature, but is applied here to the case of air pollution. It uses a computer model involving ambient pollution and temperature input to simulate the exposures experienced by individuals in an urban area, while incorporating the mechanisms that determine exposures. The output from the model comprises a set of daily exposures for a sample of individuals from the population of interest. These daily exposures are approximated by parametric distributions so that the predictive exposure distribution of a randomly selected individual can be generated. These distributions are then incorporated into a hierarchical Bayesian framework (with inference using Markov chain Monte Carlo simulation) in order to examine the relationship between short-term changes in exposures and health outcomes, while making allowance for long-term trends, seasonality, the effect of potential confounders and the possibility of ecological bias. The paper applies this approach to particulate pollution (PM10) and respiratory mortality counts for seniors in greater London (≥65 years) during 1997. Within this substantive epidemiological study, the effects on health of ambient concentrations and (estimated) personal exposures are compared. The proposed model incorporates within day (or between individual) variability in personal exposures, which is compared to the more traditional approach of assuming a single pollution level applies to the entire population for each day. Effects were estimated using single lags and distributed lag models, with the highest relative risk, RR=1.02 (1.01–1.04), being associated with a lag of two days ambient concentrations of PM10. Individual exposures to PM10 for this group (seniors) were lower than the measured ambient concentrations with the corresponding risk, RR=1.05 (1.01–1.09), being higher than would be suggested by the traditional approach using ambient concentrations

Multivariate analysis of monsoon seasonal variation and prediction of particulate matter episode using regression and hybrid models

Prediction of particulate matter (PM10) episode in advance enables for better preparation to avert and reduce the impact of air pollution ahead of time. This is possible with proper understanding of air pollutants and the parameters that influence its pattern. Hence, this study analysed daily average PM10, temperature (T), humidity (H), wind speed and wind direction data for 5 years (2006–2010), from two industrial air quality monitoring stations. These data were used to evaluate the impact of meteorological parameters and PM10 in two peculiar seasons: south-west monsoon and north-east monsoon seasons, using principal component analysis (PCA). Subsequently, lognormal regression (LR), multiple linear regression (MLR) and principal component regression (PCR) methods were used to forecast next-day average PM10 concentration level. The PCA result (seasonal variability) showed that peculiar relationship exists between PM10 pollutants and meteorological parameters. For the prediction models, the three methods gave significant results in terms of performance indicators. However, PCR had better predictability, having a higher coefficient of determination (R2) and better performance indicator results than LR and MLR methods. The outcomes of this study signify that PCR models can be effectively used as a suitable format in predicting next-day average PM10 concentration levels

Detection and attribution of an anomaly in terrestrial photosynthesis in Europe during the COVID-19 lockdown

Carbon dioxide (CO2) uptake by plant photosynthesis, referred to as gross primary production (GPP) at the ecosystem level, is sensitive to environmental factors, including pollutant exposure, pollutant uptake, and changes in the scattering of solar shortwave irradiance (SWin) - the energy source for photosynthesis. The 2020 spring lockdown due to COVID-19 resulted in improved air quality and atmospheric transparency, providing a unique opportunity to assess the impact of air pollutants on terrestrial ecosystem functioning. However, detecting these effects can be challenging as GPP is influenced by other meteorological drivers and management practices. Based on data collected from 44 European ecosystem-scale CO2 flux monitoring stations, we observed significant changes in spring GPP at 34 sites during 2020 compared to 2015-2019. Among these, 14 sites showed an increase in GPP associated with higher SWin, 10 sites had lower GPP linked to atmospheric and soil dryness, and seven sites were subjected to management practices. The remaining three sites exhibited varying dynamics, with one experiencing colder and rainier weather resulting in lower GPP, and two showing higher GPP associated with earlier spring melts. Analysis using the regional atmospheric chemical transport model (LOTOS-EUROS) indicated that the ozone (O-3) concentration remained relatively unchanged at the research sites, making it unlikely that O-3 exposure was the dominant factor driving the primary production anomaly. In contrast, SWin increased by 9.4 % at 36 sites, suggesting enhanced GPP possibly due to reduced aerosol optical depth and cloudiness. Our findings indicate that air pollution and cloudiness may weaken the terrestrial carbon sink by up to 16 %. Accurate and continuous ground-based observations are crucial for detecting and attributing subtle changes in terrestrial ecosystem functioning in response to environmental and anthropogenic drivers