Monitoring the effect of air pollution episodes on health care consultations and ambulance call-outs in England during March/April 2014: A retrospective observational analysis

Effect of ambient temperature and other environmental factors on stroke emergency department visits in Beijing: A distributed lag non-linear model

Background

Most studies have focused on the relationship between ambient temperature and stroke mortality, but studies on the relationship between ambient temperature and stroke occurrence are still limited and inconsistent.

Objective

This study aimed to analyze the effect of ambient temperature and other environmental factors on emergency stroke visits in Beijing.

Methods

Our study utilized stroke visit data from the Beijing Red Cross Emergency Medical Center during 2017-2018, and applied a generalized additive model (GAM) as well as a distributed lag non-linear model (DLNM), respectively, regarding the direct, lagged, and cumulative effects of ambient temperature alone and with correction for other environmental factors on stroke occurrence.

Results

With a total of 26,984 emergency stroke patients in 2017-2018, both cold and hot effects were observed and weakened after correction for other environmental factors. Compared to the reference temperature, in the multi-factor model, extreme cold (-10°C) reached a maximum relative risk (RR) of 1.20 [95% Confidence Interval (CI): 1.09, 1.32] at lag 14 days, and extreme hot (30°C) had a maximum RR of 1.07 (95% CI: 1.04, 1.11) at lag 6 days. The cumulative effect of extreme cold reached a maximum of 2.02 (95% CI: 1.11, 3.67) at lag 0-14 days, whereas the cumulative effect of extreme hot temperature is greatest at lag 0-10 days, but no statistically significant effect was found. In addition, ischemic stroke patients, the elderly, and males were more susceptible to the effects of cold temperature.

Conclusions

There is a non-linear relationship between ambient temperature and stroke occurrence, with cold temperature having a greater and longer-lasting impact than hot temperature.

Simulation of Traffic-Born Pollutant Dispersion and Personal Exposure Using High-Resolution Computational Fluid Dynamics

Road vehicles are a large contributor to nitrogen oxides (NOx) pollution. The routine roadside monitoring stations, however, may underrepresent the severity of personal exposure in urban areas because long-term average readings cannot capture the effects of momentary, high peaks of air pollution. While numerical modelling tools historically have been used to propose an improved distribution of monitoring stations, ultra-high resolution Computational Fluid Dynamics models can further assist the relevant stakeholders in understanding the important details of pollutant dispersion and exposure at a local level. This study deploys a 10-cm-resolution CFD model to evaluate actual high peaks of personal exposure to NOx from traffic by tracking the gases emitted from the tailpipe of moving vehicles being dispersed towards the roadside. The investigation shows that a set of four Euro 5-rated diesel vehicles travelling at a constant speed may generate momentary roadside concentrations of NOx as high as 1.25 mg/m3, with a 25% expected increase for doubling the number of vehicles and approximately 50% reduction when considering Euro 6-rated vehicles. The paper demonstrates how the numerical tool can be used to identify the impact of measures to reduce personal exposure, such as protective urban furniture, as traffic patterns and environmental conditions change.

Estimates of population exposure to atmospheric pollution and health-related externalities in a real city: The impact of spatial resolution on the accuracy of results

Health impacts of atmospheric pollution is an important issue in urban environments. Its magnitude depends on population exposure which have been frequently estimated by considering different approaches relating pollutant concentration and population exposed to it. However, the uncertainties due to the spatial resolution of the model used to estimate the pollutant concentration or due to the lack of representativeness of urban air quality monitoring station (AQMS) have not been evaluated in detail. In this context, NO₂ annual average concentration at pedestrian level in the whole city of Pamplona (Spain) modelled at high spatial resolution (~1 m) by Computational Fluid Dynamic (CFD) simulations is used to estimate the total population exposure and health-related externalities by using different approaches. Air pollutant concentration and population are aggregated at different spatial resolutions ranging from a horizontal grid cell size of 100 m × 100 m to a coarser resolution where the whole city is covered by only one cell (6 km × 5 km). In addition, concentrations at AQMS locations are also extracted to assess the representativeness of those AQMS. The case with a spatial resolution of 100 m × 100 m for both pollutant-concentration distribution and population data is used as a reference (Base case) and compared with those obtained with the other approaches. This study indicates that the spatial resolution of concentration and population distribution in the city should be 1 km × 1 km or finer to obtain appropriate estimates of total population exposure (underestimations <13%) and health-related externalities (underestimations <37%). For the cases with coarser resolutions, a strong underestimation of total population exposure (>31%) and health-related externalities (>76%) was found. On the other hand, the use of AQMS concentrations can induce important errors due to the limited spatial representativeness, in particular in terms of population exposure.

Chemical characterization and source identification of PM2.5 in Luoyang after the clean air actions

Luoyang is a typical heavy industrial city in China, with a coal-dominated energy structure and serious air pollution. Following the implementation of the clean air actions, the physicochemical characteristics and sources of PM_2.5 have changed. A comprehensive study of PM_2.5 was conducted from October 16, 2019 to January 23, 2020 to evaluate the effectiveness of previous control measures and further to provide theory basis for more effective policies in the future. Results showed that the aerosol pollution in Luoyang in autumn and winter is still serious with the average concentration of 91.1 μg/m³, although a large reduction (46.9%) since 2014. With the contribution of nitrate increased from 12.5% to 25.1% and sulfate decreased from 16.7% to 11.2%, aerosol pollution has changed from sulfate-dominate to nitrate-dominate. High NO₃^-/SO₄^2- ratio and the increasing of NO₃^-/SO₄^2- ratio with the aggravation of pollution indicating vehicle exhaust playing an increasingly important role in PM_2.5 pollution in Luoyang, especially in the haze processes. Secondary inorganic ions contributed significantly to the enhancement of PM_2.5 during the pollution period. The high value of Cl^-/Na⁺ and EC concentration indicate coal combustion in Luoyang is still serious. The top three contributor sources were secondary inorganic aerosols (33.3%), coal combustion (13.6%), and industrial emissions (13.4%). Close-range transport from the western and northeastern directions were more important factors in air pollution in Luoyang during the sampling period. It is necessary to strengthen the control of coal combustion and reduce vehicle emissions in future policies.

Sources of PM2.5-bound water soluble ions at EMEP's Auchencorth Moss (UK) supersite revealed by 3D-Concentration Weighted Trajectory (CWT) model

The Concentration Weighted Trajectory (CWT) model is a well-known tool which combines the residence time (trajectory points) of air masses over specific regions with ambient concentrations of air pollutants, aiming to identify potential long range transport impacts. An upgraded 3D-version of CWT model (3D-CWT), investigating not only the geographical origin of the exogenous emissions but also the altitudinal layers in which the transport occurs, was developed and coupled with PM_2.5-bound concentrations of water soluble ions (nss- SO₄^-2 (non-sea salt sulfates), NO₃^-, Cl^-, NH₄⁺, Na⁺, Mg⁺², Ca⁺² and K⁺) for the years 2017-2018, derived by the Auchencorth Moss supersite in Southeast Scotland, United Kingdom (UK). The 3D-CWT model was implemented in two distinct altitudinal layers above ground level (0 m ≤ Layer 1 < 1000 m, 1000 m ≤ Layer 2 < 2000 m), because few trajectory points exceeded the 2000 m limit. Transport of Secondary Inorganic Aerosols (SIA) from South - Southeast England were detected in both vertical layers, affecting SO₄^-2, NO₃^-, and NH₄⁺ levels, whilst SIA intrusions from Northwest Europe were detected in Layer 2. Sea salt particle transport from North Atlantic and the North Sea, comprising Cl^-, Na⁺ and Mg⁺², were detected in both layers whilst K⁺ contributions from Southeast England were also detected in both layers, suggesting also impacts from biomass burning. Moreover particle transport of a crustal origin, marked by Ca⁺² enhancement, mainly occurred in layer 1 and included soil/dust resuspension from areas around the station and infrequent dust intrusions from the Sahara desert.

Impact of exposure to mine fire emitted PM2.5 on ambulance attendances: A time series analysis from the Hazelwood Health Study

BACKGROUND

For six weeks from February 9, 2014, smoke and ash from a fire in the Morwell open cut brown coal mine adjacent to the Hazelwood power station covered parts of the Latrobe Valley, in south eastern Australia.

AIMS

To evaluate the health impact of the mine fire, daily counts of ambulance attendances from July 2010 to March 2015 were analysed.

METHODS

Time series models were used to evaluate the relative risk of ambulance attendances during the mine fire, in comparison with the remainder of the analysis period, and to also assess the risk of ambulance attendances associated with lagged effects of exposure to mine fire-related PM_2.5 levels. The models controlled for factors likely to influence ambulance attendances including seasonality, long-term temporal trends, day of the week, daily maximum temperature and public holidays.

RESULTS

A 10 μg/m³ increase in fire-related PM_2.5 was found to be associated with a 42% (95%CI: 14-76%) increase in ambulance attendances for respiratory conditions and a 7% (0-14%) increase in all ambulance attendances over a 20-day lag period. A smaller effect associated with exposure to fire-related PM_2.5 was identified when assuming shorter lag effects. Similar results were identified when assessing whether ambulance attendances increased during the 30-day mine fire period. There was a 15% (8-21%) increased risk of ambulance attendances for all conditions and a 47% (19-81%) increased risk for respiratory conditions during the mine fire period.

CONCLUSIONS

Exposure to smoke and ash from a fire in an open cut brown coal mine was associated with increased ambulance attendances, particularly for respiratory conditions. These findings guide the development and implementation of effective and timely strategies and health service planning to respond and mitigate health risks that arise in affected communities during future major air pollution episodes.

Estimates of pedestrian exposure to atmospheric pollution using high-resolution modelling in a real traffic hot-spot

Atmospheric pollution is a very relevant risk for the human health, in particular in urban environments, where most people lives and high levels of pollution are found. Population exposure is traditionally estimated through concentration recorded at air quality monitoring stations (AQMS) or modelled at a spatial resolution of the order of 1 km². However, these methodologies have limitations in urban areas where strong gradients of concentration, even in the same street, exist. In addition, the movements of pedestrians make difficult to compute reliable estimates of pollutant concentration to which people are exposed to. In this context, the main objective of this study is to estimate the exposure of pedestrians to ambient nitrogen oxides (NOx) concentrations with high spatial resolution in a real urban traffic hot-spot under different methodologies. To achieve this objective, a novel methodology which combines high-resolution NOx concentrations from computational fluid dynamic (CFD) simulations with the pedestrian flows obtained by pedestrian mobility microsimulations is applied to an urban area of Madrid, Spain. High-resolution maps show pedestrian exposure peaks, at bus stops and crosswalks, that cannot be captured by the simpler methods based on spatial average concentration (SAC) or concentration measured in an AQMS. Total daily exposure obtained is 1.19 · 10⁹ person s μg m^-3, while SAC and AQMS concentration methods yielded 9-23% and 30-40% lower values. In conclusion, the proposed methodology allows to determine the areas with higher exposure in order to design local strategies to reduce the impact on human health. In addition, from a more general point of view, the total exposure in the studied area is better estimated by using spatial average concentration than through concentration recorded by AQMS. The assessment of the spatial representative of AQMS becomes necessary to use AQMS concentration to evaluate air quality and population exposure of an urban area.

Short-term effects of air pollution on exacerbations of allergic asthma in Užice region, Serbia

Introduction

Many time-series studies have shown a positive association between air pollution and asthma exacerbation. However, till now only one study in Serbia has examined this relationship.

Aim

To examine the associations between air pollution and asthma emergency department (ED) visits in the Užice region, Serbia.

Material and methods

A time-stratified case-crossover design was applied to 424 ED visits for asthma exacerbation that occurred in the Užice region, Serbia, in 2012–2014. Data about ED visits were routinely collected in the Užice Health Centre. The daily average concentrations of particulate matter (PM_2.5 and PM₁₀), sulphur dioxide (SO₂), nitrogen dioxide (NO₂), and black carbon (BC) were measured by automatic ambient air quality monitoring stations. Odds ratios and their corresponding 95% confidence intervals were estimated using conditional logistic regression adjusted for the potential confounding influence of weather variables (temperature, humidity and air pressure).

Results

Statistically significant associations were observed between ED visits for asthma and 3-day lagged exposure to BC (OR = 3.23; 95% CI: 1.05–9.95), and between ED visits for asthma with coexisting allergic rhinitis and 0-day lag exposure to NO₂ (OR = 1.57; 95% CI: 0.94–2.65), 2-day lag exposure to SO₂ (OR = 1.97; 95% CI: 1.02–3.80), and 3-day lag exposure to PM₁₀ (OR = 2.38; 95% CI: 1.17–4.84).

Conclusions

Exposure to ambient air pollution in the Užice region increases the risk of ED visits for asthma, particularly during the heating season.

Impact of extreme temperatures on ambulance dispatches in London, UK

BACKGROUND

Associations between extreme temperatures and health outcomes, such as mortality and morbidity, are often observed. However, relatively little research has investigated the role of extreme temperatures upon ambulance dispatches.

METHODS

A time series analysis using London Ambulance Service (LAS) incident data (2010-2014), consisting of 5,252,375 dispatches was conducted. A generalized linear model (GLM) with a quasi-likelihood Poisson regression was applied to analyse the associations between ambulance dispatches and temperature. The 99^th (22.8°C) and 1^st (0.0°C) percentiles of temperature were defined as extreme high and low temperature. Fourteen categories of ambulance dispatches were investigated, grouped into 'respiratory' (asthma, dyspnoea, respiratory chest infection, respiratory arrest and chronic obstructive pulmonary disease), 'cardiovascular' (cardiac arrest, chest pain, cardiac chest pain RCI, cardiac arrhythmia and other cardiac problems) and 'other' non-cardiorespiratory (dizzy, alcohol related, vomiting and 'generally unwell') categories. The effects of long-term trends, seasonality, day of the week, public holidays and air pollution were controlled for in the GLM. The lag effect of temperature was also investigated. The threshold temperatures for each category were identified and a distributed lag non-linear model (DLNM) was reported using relative risk (RR) values at 95% confidence intervals.

RESULTS

Many dispatch categories show significant associations with extreme temperature. Total calls from 999 dispatches and 'generally unwell' dispatch category show significant RRs at both low and high temperatures. Most respiratory categories (asthma, dyspnoea and RCI) have significant RRs at low temperatures represented by with estimated RRs ranging from 1.392 (95%CI: 1.161-1.699) for asthma to 2.075 (95%CI: 1.673-2.574) for RCI. The RRs for all other non-cardiorespiratory dispatches were often significant for high temperatures ranging from 1.280 (95% CI: 1.128-1.454) for 'generally unwell' to 1.985 (95%CI: 1.422-2.773) for alcohol-related. For the cardiovascular group, only chest pain dispatches reported a significant RR at high temperatures.

CONCLUSIONS

Ambulance dispatches can be associated with extreme temperatures, dependent on the dispatch category. It is recommended that meteorological factors are factored into ambulance forecast models and warning systems, allowing for improvements in ambulance and general health service efficiency.

Impact of Media Reports and Environmental Pollution on Health and Health Expenditure Efficiency

Over the past few decades, China’s rapid economic, energy, and industrial developments have caused serious environmental damage. However, as there are large resource, energy use, economic, and environmental damage differences across Chinese regions, the Chinese government is seeking to reduce city pollution across the country. Most previous analyses have only looked at these issues on a single level; for example, the impact of environmental pollution on health, or energy and environmental efficiency analyses, but there have been few studies that have conducted overall analyses. Further, many of the methods that have been used in previous research have employed one-stage radial or non-radial analyses without considering regional differences. Therefore, this paper developed a meta undesirable two-stage EBM DEA model to analyze the energy, environment, health, and media communication efficiencies in 31 Chinese cities, from which it was found that the productivity efficiency in most cities was better than the health treatment efficiencies, the GDP and fixed asset efficiency improvements were small, the air quality index (AQI) and CO₂ efficiencies varied widely between the cities, media report and governance inputs were generally inefficient, the birth rate efficiencies were better than the respiratory disease efficiencies, and the technical gap was best in Guangzhou, Shanghai, and Lhasa. Also, it found that high-income cities have a higher technology gap than upper middle–income cities, and media reports efficiency have a high correlation with respiratory diseases and CO₂.

Syndromic surveillance using ambulance transfer data in Tokyo, Japan

Bioterrorism attacks become more probable when important high-profile international or political events are held, such as G7 summit meetings or mass gathering events including Olympic and Paralympic games and FIFA World Cup tournaments. Outbreaks of infectious disease and widespread incidents of food poisoning are also public health concerns at such times. In Japan, the Tokyo Metropolitan Government operates Ambulance Transfer Syndromic Surveillance (ATSS), which can help monitor such incidents. The present study presents and assesses the ATSS framework. During the study period of October 2017 through November 2018, we monitored 33 areas for symptoms of 9 categories: vomiting/nausea, dizziness, palpitation, unconsciousness, breathing disorder, fever, spasm/paralysis, collapse/weakness, and bloody emesis/nasal hemorrhage. Among all symptoms, we found 9929 low-level aberrations, 2537 medium-level aberrations, and 577 high-level aberrations, with respective frequencies of 9.2%, 2.3%, and 0.5%. Of those, Tokyo Metropolitan Institute of Public Health reported the information to Tokyo Metropolitan Government 28 times during the period. Of the 28 identified clusters, Tokyo Metropolitan Government judged the necessity for investigating 7. All of those were investigated at hospitals by the jurisdictional public health center. Because ATSS covers almost the entire Tokyo metropolitan area, with about 13.8 million residents, it is definitely the largest syndromic surveillance in the world.

Characteristics of chemical composition and seasonal variations of PM2.5 in Shijiazhuang, China: Impact of primary emissions and secondary formation

North China registers frequent air pollution episodes from high PM_2.5 concentrations. Shijiazhuang is located at the intensive industrial zone of this region, but there is insufficient data on the chemical composition of PM_2.5 and its sources in this city. In this study, the chemical and seasonal characteristics of PM_2.5 in Shijiazhuang were investigated based on 12-h integrated PM_2.5 measurements made over eight 1-month periods in each season between June 2014 and April 2016 (486 samples). The eight-season average concentration of PM_2.5 was 138.8 μg m^-3, and the major chemical components were secondary inorganic aerosol (SIA) species of sulfate, nitrate, and ammonium (41.5%), followed by organic matter (25.9%). The mass concentration and most of the chemical components of PM_2.5 showed clear seasonal variation, with a winter-high and summer-low pattern. SO₄^2- and NO₃^- were the dominant components at each pollution level in summer and autumn (18.1%-30.6% and 14.2%-27.0%, respectively). Sufficient gaseous oxidants (O₃) concentrations and suitable meteorology conditions were observed in these two seasons. Highest SOR (0.61), SO₄^2-/EC(10.8) and NOR (0.58), NO₃^-/EC (5.9) were found in summer and autumn, which indicated intense secondary transformation in these two seasons. Organic matter was the dominant species in winter, which increased from 17.1 μg m^-3 for clean days (28.7% of PM_2.5) to 169.1 μg m^-3 (38.4% of PM_2.5). The accumulation of primary emissions (coal combustion and biomass burning) was responsible for the increasing OM trend (especially for POC). The highest and leading proportion of mineral dust occurred in spring (20.3%-46.5%) as a result of higher wind speeds (up to 3 m/s). Potential source contribution function (PSCF) analyses implied that the border areas of Hebei, Henan and Shandong Provinces, together with the central area of Shanxi Province, contributed significantly to the PM_2.5 pollution in Shijiazhuang, especially in autumn and winter.

Twitter mining using semi-supervised classification for relevance filtering in syndromic surveillance

We investigate the use of Twitter data to deliver signals for syndromic surveillance in order to assess its ability to augment existing syndromic surveillance efforts and give a better understanding of symptomatic people who do not seek healthcare advice directly. We focus on a specific syndrome-asthma/difficulty breathing. We outline data collection using the Twitter streaming API as well as analysis and pre-processing of the collected data. Even with keyword-based data collection, many of the tweets collected are not be relevant because they represent chatter, or talk of awareness instead of an individual suffering a particular condition. In light of this, we set out to identify relevant tweets to collect a strong and reliable signal. For this, we investigate text classification techniques, and in particular we focus on semi-supervised classification techniques since they enable us to use more of the Twitter data collected while only doing very minimal labelling. In this paper, we propose a semi-supervised approach to symptomatic tweet classification and relevance filtering. We also propose alternative techniques to popular deep learning approaches. Additionally, we highlight the use of emojis and other special features capturing the tweet's tone to improve the classification performance. Our results show that negative emojis and those that denote laughter provide the best classification performance in conjunction with a simple word-level n-gram approach. We obtain good performance in classifying symptomatic tweets with both supervised and semi-supervised algorithms and found that the proposed semi-supervised algorithms preserve more of the relevant tweets and may be advantageous in the context of a weak signal. Finally, we found some correlation (r = 0.414, p = 0.0004) between the Twitter signal generated with the semi-supervised system and data from consultations for related health conditions.

A Two-stage Dynamic Undesirable Data Envelopment Analysis Model Focused on Media Reports and the Impact on Energy and Health Efficiency

Past research on energy and environmental issues in China has generally focused on energy and environmental efficiencies with no models having included the public health associations or the role of the media. Therefore, to fill this research gap, this paper used a modified Undesirable Dynamic Network model to analyze the efficiency of China’s energy, environment, health and media communications, from which it was found that the urban production efficiency stage was better than the health treatment stage, and that the energy efficiencies across the Chinese regions varied significantly, with only Beijing, Guangzhou, Lhasa and Nanning being found to have high efficiencies. Large urban gaps and low efficiencies were found for health expenditure, with the best performances being found in Fuzhou, Guangzhou, Haikou, Hefei, Nanning, and Urumqi. The regions with the best media communication efficiencies were Fuzhou, Guangzhou, Haikou, Hefei, Lhasa, Nanning and Urumqi, and the cities with the best respiratory disease efficiencies were Fuzhou, Guangzhou, Haikou, Lhasa, Nanning, Wuhan, Urumqi, Xian, and Yinchuan. Overall, significant efficiency improvements were needed in health expenditure and in particular in respiratory diseases as there were major differences across the country.

Ambient Particulate Matter and Paramedic Assessments of Acute Diabetic, Cardiovascular, and Respiratory Conditions

Supplemental Digital Content is available in the text.

Background:

Ambulance data provide a useful source of population-based and spatiotemporally resolved information for assessing health impacts of air pollution in nonhospital settings. We used the clinical records of paramedics to quantify associations between particulate matter (PM_2.5) and diabetic, cardiovascular, and respiratory conditions commonly managed by those responding to calls for emergency ambulance services.

Methods:

We evaluated 394,217 paramedic assessments from three states in Southeastern Australia (population 13.2 million) and daily PM_2.5 concentrations modeled at 5 km resolution from 2009 to 2014. We used a time-stratified, case-crossover analysis adjusted for daily meteorology to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for each clinical outcome per 10 µg/m³ increase in daily PM_2.5 at lags from 0 to 2 days.

Results:

Increased PM_2.5 was associated with increased odds of paramedic assessments of hypoglycemia (OR = 1.07; 95% CI = 1.02, 1.12, lag 0), arrhythmia (OR = 1.05; 95% CI = 1.02, 1.09, lag 0), heart failure (OR = 1.07; 95% CI = 1.02, 1.12, lag 1), faint (OR = 1.09; 95% CI = 1.04–1.13, lag 0), asthma (OR = 1.06; 95% CI = 1.01, 1.11, lag 1), chronic obstructive pulmonary disease (OR = 1.07; 95% CI = 1.01, 1.13, lag 1), and croup (OR = 1.09; 95% CI = 1.02, 1.17). We did not identify associations with cerebrovascular outcomes.

Conclusions:

Ambulance data enable the evaluation of important clinical syndromes that are often initially managed in nonhospital settings. Daily PM_2.5 was associated with hypoglycemia, faint, and croup in addition to the respiratory and cardiovascular outcomes that are better established.

Retrospective observational study of emergency department syndromic surveillance data during air pollution episodes across London and Paris in 2014

INTRODUCTION

Poor air quality (AQ) is a global public health issue and AQ events can span across countries. Using emergency department (ED) syndromic surveillance from England and France, we describe changes in human health indicators during periods of particularly poor AQ in London and Paris during 2014.

METHODS

Using daily AQ data for 2014, we identified three periods of poor AQ affecting both London and Paris. Anonymised near real-time ED attendance syndromic surveillance data from EDs across England and France were used to monitor the health impact of poor AQ.Using the routine English syndromic surveillance detection methods, increases in selected ED syndromic indicators (asthma, difficulty breathing and myocardial ischaemia), in total and by age, were identified and compared with periods of poor AQ in each city. Retrospective Wilcoxon-Mann-Whitney tests were used to identify significant increases in ED attendance data on days with (and up to 3 days following) poor AQ.

RESULTS

Almost 1.5 million ED attendances were recorded during the study period (27 February 2014 to 1 October 2014). Significant increases in ED attendances for asthma were identified around periods of poor AQ in both cities, especially in children (aged 0-14 years). Some variation was seen in Paris with a rapid increase during the first AQ period in asthma attendances among children (aged 0-14 years), whereas during the second period the increase was greater in adults.

DISCUSSION

This work demonstrates the public health value of syndromic surveillance during air pollution incidents. There is potential for further cross-border harmonisation to provide Europe-wide early alerting to health impacts and improve future public health messaging to healthcare services to provide warning of increases in demand.

Mortality and emergency hospitalizations associated with atmospheric particulate matter episodes across the UK in spring 2014

Exposure to particulate air pollution is known to have negative impacts on human health. Long-term exposure to anthropogenic particulate matter is associated with the equivalent of around 29,000 deaths a year in the UK. However, short-lived air pollution episodes on the order of a few days are also associated with increased daily mortality and emergency hospital admissions for respiratory and cardiovascular conditions. The UK experienced widespread high levels of particulate air pollution in March-April 2014; observations of hourly mean PM_2.5 concentrations reached up to 83μgm^-3 at urban background sites. We performed an exposure and health impact assessment of the spring air pollution, focusing on two episodes with the highest concentrations of PM_2.5 (12-14 March and 28 March-3 April 2014). Across these two episodes of elevated air pollution, totalling 10days, around 600 deaths were brought forward from short-term exposure to PM_2.5, representing 3.9% of total all-cause (excluding external) mortality during these days. Using observed levels of PM_2.5 from other years, we estimate that this is 2.0 to 2.7 times the mortality burden associated with typical urban background levels of PM_2.5 at this time of year. Our results highlight the potential public health impacts and may aid planning for health care resources when such an episode is forecast.