0
‫ד"ר חוה פרץ,‬     ‫אוניברסיטת תל אביב, החוג לאפידמיולוגיה‬                ‫לוסיה ברגובוי-ילין,‬‫אוני תל אביב, סטודנטית בב...
‫במחקרים אפידמיולוגים בעולם המערבי בעבר נמצא:‬         ‫שמגורים בסמוך לצירי תחבורה קשורים בעליה בסימפטומים‬         ‫נשימת...
‫שינויים במרוצת הזמן:‬‫עליה במערב ב ‪RESIRATORY ALLERGIC DISEASES‬‬             ‫‪‬‬ ‫שינויים ברמות החשיפה- תקינה, שפור ט...
   Bibliographic search: articles published 2005-2011 :    ◦ PubMed (NLM and NIH), Embase.com (Elsevier).    ◦ ScienceDir...
◦ Airborne particulates: PM10, PM2.5, PM1.0 (ultra-fine)◦ Nitrogen oxides (NOX): NO, NO2◦ Sulfur dioxide (SO2)◦ Carbon mon...
‫מחקרי חתך‬ Asthma, respiratory outcomes, allergy (Nicolai et al 2003) symptoms of asthma and allergic sensitisation  (...
‫מחקרי חתך‬   Large studies - the ISAAC (International Study of Asthma and Allergies in    Childhood) protocol:     Muni...
‫מחקרי עוקבה‬   Windsor, Ontario, CA: 182 asthmatic elementary schoolchildren (9-14 yrs).    11.10–11.12/14.11–11.12.2005...
    An environmental questionnaire (EQ(    A question: Frequency of truck traffic on the street of residence.“How often d...
   Instruments: At the school level, the inter-variability of PM2.5 and NO2    assessments during the survey span was est...
H-Graveland et al 2007 (cont.)
 Age, sex BMI and nationality ethnic group Socioeconomic status. Time related variables: chronological time, season, mo...
1. World-wide: Brunekreef et al 2009
Methods:   Sample: 13- to 14-year-old , n=325572   and 6- to 7-year-old children , n=197,515 across the world.   Exposur...
Association between self-reported truck traffic on the street of residence and symptomsin 6- to 7-year-old children
Methods:   Sample: Random samples of schoolchildren (n=7,509, response  rate 83.7%) 5 in Munich Germany     Exposure: tr...
Table 4: Respiratory and atopic outcomes in relation to traffic counts (High exposure tertile) in the area of residence   ...
Table 4: Respiratory and atopic outcomes in relation to traffic counts (High exposure tertile) in the area of residence   ...
Methods: Sample: 5338 school children (10.4 years) attending 108 randomly  chosen schools in 6 French cities   Exposure:...
Table 4: Odds ratios (95% confidence interval) of allergic and respiratory morbidity by high vs. low concentrations of    ...
3. Annesi-Maesano et al 2007 (cont.)Table 5: Odds ratios (95% confidence interval) of allergic sensitisation by high vs. l...
Methods: Sample: 812 children from nine Dutch schools within 400 m of  motorways.   Exposure: Daily levels of PM10, obt...
H-Graveland et al 2007     Figure 2: Adjusted* geometric means ratios with 95% CIs for the associations between traffic ch...
5. Dales et al 2009Methods:   Sample: 182 elementary schoolchildren with physician-diagnosed    asthma   Exposure: city ...
5. Dales et al 2009Mean (95% confidence interval) for diurnal change in forced expiratory volume in 1 s (FEV1)associated w...
Methods:   Sample: 861 children with persistent asthma in 7 US urban    communities   Exposure: Daily pollution measurem...
Table 3: Mean (95% CI) change in pulmonary function parameter at the 90th percentile of pollutant concentration relative t...
Methods:   Sample: 249 subjects (57 asthmatics, 192 nonasthmatics)    age- high schools   Exposure: BC and PM2.5 monitor...
Table 4: ORsa (95% CI) for respiratory symptoms and use of medication for asthma associated with an IQRbincrease in pollut...
Methods:   Sample: 53 subjects with asthma, 9-18 y in Los-Angeles   Exposure: Personal hourly PM2.5 mass, 24-hr PM EC an...
Figure 2: Adjusted single- and two-pollutant models           Figure 3: Adjusted single- and two-pollutant models(coeffici...
Methods:   Sample: 45 grade children with asthma at four South Bronx    schools (10 children per school)   Exposure: Dai...
Table 2: Mixed model estimates of lung function decrements associated with personal and school-site pollutants      a from...
Figure 1: Relative risks of cough, wheeze, shortness of breath and Figure 2: Relative risks of cough, wheeze, shortness of...
   World-wide: Higher exposure to self-reported truck traffic on the street of    residence is associated with increased ...
   In Canadian children with asthma: Relatively low concentrations of urban air pollution worsen    lung function over a ...
 זיהום אוויר מתחבורה והשפעתו על תחלואה בקרב ילדי בתי ספר
 זיהום אוויר מתחבורה והשפעתו על תחלואה בקרב ילדי בתי ספר
Upcoming SlideShare
Loading in...5
×

זיהום אוויר מתחבורה והשפעתו על תחלואה בקרב ילדי בתי ספר

1,375

Published on

סקירה המתמקדת בממצאי תשעה מחקרים אפידמיולוגיים עדכניים שנעשו בעולם ועוסקים בקשר שבין חשיפה קצרת טווח לזיהום אוויר מתחבורה ותחלואה בקרב ילדי בי"ס (ברמה עולמית, אירופה, קנדה ובארה"ב).
מצגת זו הוצגה במסגרת הרצאה אשר ניתנה בפני הפורום לבריאות וסביבה, המשרד להגנת הסביבה, על ידי ד"ר חוה פרץ מהמחלקה לאפידמיולוגיה, אוניברסיטת תל אביב.

Published in: Health & Medicine, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
1,375
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
5
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Transcript of " זיהום אוויר מתחבורה והשפעתו על תחלואה בקרב ילדי בתי ספר"

  1. 1. ‫ד"ר חוה פרץ,‬ ‫אוניברסיטת תל אביב, החוג לאפידמיולוגיה‬ ‫לוסיה ברגובוי-ילין,‬‫אוני תל אביב, סטודנטית בבי"ס פורטר ללימודי סביבה‬ ‫מתמחה, הקואליציה לבריאות הציבור‬‫הפורום לבריאות וסביבה: מרחק מוסדות חינוך מכבישים‬ ‫המשרד להגנת הסביבה, מפגש 1102.50.71‬
  2. 2. ‫במחקרים אפידמיולוגים בעולם המערבי בעבר נמצא:‬ ‫שמגורים בסמוך לצירי תחבורה קשורים בעליה בסימפטומים‬ ‫נשימתיים ,בעליה בשעורי רגישות ‪ SENSITIZATION‬וירידה‬ ‫בתפקודי ראות -- בילדים‬ ‫חולשות עיקריות:‬ ‫- תוקף הערכת החשיפה , ‪ , outdoor –indoor‬חשיפה אישית?‬ ‫מנה‬ ‫- תוקף הערכת התחלואה , אוביקטיביות?‬ ‫- מחקרי חתך, חד פעמיות המדידה (השתנות עונתית וכד)‬ ‫- מדגמים קטנים, ייצוגיות?‬ ‫- חשיפות גבוהות, 0‪lag‬‬‫- משתנים מתערבים- מצב סוציואקונומי, גזע, נגישות לשרותי בריאות .‬ ‫- אוכלוסיות רגישות, כגון- אסטמתיים‬ ‫- הפרדה בין מזהמים, חומר חלקיקי- גודל ותכולת חלקיקים‬
  3. 3. ‫שינויים במרוצת הזמן:‬‫עליה במערב ב ‪RESIRATORY ALLERGIC DISEASES‬‬ ‫‪‬‬ ‫שינויים ברמות החשיפה- תקינה, שפור טכנולוגי ומודעות‬ ‫‪‬‬ ‫שינויים בנפח התנועה‬ ‫‪‬‬ ‫הבנה של המכאניזם הביולוגי : חשיפה-תחלואה, תמיכה‬ ‫‪‬‬ ‫בקשר סיבתי‬
  4. 4.  Bibliographic search: articles published 2005-2011 : ◦ PubMed (NLM and NIH), Embase.com (Elsevier). ◦ ScienceDirect, Informaworld, SpringerLink, Science, Scholar Google. Key words: short term/acute exposure; traffic-related air pollution; schoolchildren; health ; health-outcome Inclusion: 9 studies on ◦ Exposure to outdoor and indoor air pollution ◦ School children between ages 6-20 years old. ◦ Study design: cross-sectional / cohort studies. ◦ Geographical ascription: (world-wide) Europe; Canada; USA;
  5. 5. ◦ Airborne particulates: PM10, PM2.5, PM1.0 (ultra-fine)◦ Nitrogen oxides (NOX): NO, NO2◦ Sulfur dioxide (SO2)◦ Carbon monoxide (CO)◦ Ozone (O3)◦ EC (elemental carbon), BC (black carbon) and OC (optical carbon) fractions (soot)◦ Benzene
  6. 6. ‫מחקרי חתך‬ Asthma, respiratory outcomes, allergy (Nicolai et al 2003) symptoms of asthma and allergic sensitisation (Annesi-Maesano et al 2007, Brunekreef et al 2009). ‫מחקרי עוקבה‬ Lung function decrements (Dales et al 2009) Lung function (Delfino et al 2008) Respiratory outcomes (O’connor et al 2008, Graveland et al 2010, Patel et al 2010, Spira-Cohen et al 2011) Lung function and atopy (Romieu et al 2008),
  7. 7. ‫מחקרי חתך‬ Large studies - the ISAAC (International Study of Asthma and Allergies in Childhood) protocol:  Munich, Germany (Phase II): 7,509.children School beginners (5–7 yrs), Fourth grade (9–11 yrs)  The French six study (6C): 5,338 elementary children (10.4±0.7 yrs).  World-wide study (Phase III): 45 developing, 30 developed countries: 315,572 children 13–14 yrs, 110 centers (46 countries) 197,515 children 6–7 yrs, 70 centers (29 countries) Netherlands, 9 Dutch schools <400 m of motorways (7-11 yrs): 812 children - 86 asthmatic, 726 non-asthmathic.
  8. 8. ‫מחקרי עוקבה‬ Windsor, Ontario, CA: 182 asthmatic elementary schoolchildren (9-14 yrs). 11.10–11.12/14.11–11.12.2005 ICAS (the Inner-City Asthma Study) protocol:  2 regions, Los-Angeles, California: 53 with mild-moderate persistent asthma (9–18 yrs). a run of 16 10-day periods of follow-up: Jul.-Dec. 2003 (Riverside), 2004 (Whittier)  7 low-income census tracts, USA: 861 asthmatic children (moderate-severe) & atopy (5-12 yrs): Aug. 1998-Jul. 2001 4 high school, urban & suburban communities, NYC: individual-level study, 249 adolescents (13-20 yrs): 57 asthmatics, 192 non-asthmatics. Different dates, 2003- 2005 4 South Bronx schools (10 children per school): 45 elementary schoolchildren with asthma (10-12 yrs), Spring 2002, Spring 2004, Fall 2004 & Spring 2005
  9. 9.  An environmental questionnaire (EQ( A question: Frequency of truck traffic on the street of residence.“How often do trucks pass through the street where you live, on weekdays?”never, seldom, frequently through the day, and almost the whole day )inISAAC phase 3), A model : using car-traffic counts and a weighting function, to account for the distance between measurement point and street, together with street characteristics (mainly per cent of time with stop-and-go conditions in the segment), Nicolai et al 2003).
  10. 10.  Instruments: At the school level, the inter-variability of PM2.5 and NO2 assessments during the survey span was estimated; concentration values obtained with our instruments at both proximity and city levels Traffic characteristics : such as truck-traffic counts and distances of the children‟s homes and school addresses from the motorways (GIS) as markers of long-term personal exposure to traffic. Sites of the National Air Quality Monitoring Network Personal air monitors: active air samplers worn in a backpack daily over the 10 consecutive days.
  11. 11. H-Graveland et al 2007 (cont.)
  12. 12.  Age, sex BMI and nationality ethnic group Socioeconomic status. Time related variables: chronological time, season, month, day of the week Climatic condition: minimum temperature) and daily mean temperature and relative humidity, Downwind school location (yes/no) . Previous day minimum temperaturePersonal Health Corticosteroid (Corticoid) Therapy or antiallergenic medicine use Previous FEV1 measurement, Family history of relevant diseases:
  13. 13. 1. World-wide: Brunekreef et al 2009
  14. 14. Methods: Sample: 13- to 14-year-old , n=325572 and 6- to 7-year-old children , n=197,515 across the world. Exposure: A question about frequency of truck traffic on the street of residence was included in an additional questionnaire. Health: symptoms of asthma, rhinoconjunctivitis, and eczema Confounders: sex, region of the world, language, gross national income, and 10 other subject-specific covariates.
  15. 15. Association between self-reported truck traffic on the street of residence and symptomsin 6- to 7-year-old children
  16. 16. Methods: Sample: Random samples of schoolchildren (n=7,509, response rate 83.7%) 5 in Munich Germany Exposure: traffic counts and an emission model which predicted soot, benzene and nitrogen dioxide (NO2), per subject Health: Intern. Study of Asthma and Allergies in Childhood phase-II protocol with skin-prick tests, measurements of specific immunoglobulin E and lung function. Confounders: age, sex, socioeconomic , family history of disease.
  17. 17. Table 4: Respiratory and atopic outcomes in relation to traffic counts (High exposure tertile) in the area of residence outcome Crude reference prevalence % (raw numbers) Adjusted OR (95% CI) Asthma 10.4 (318/3071) 1.194 (0.762–1.871) Current asthma# 5.0 (157/3124) 1.790 (1.051–3.048)§ Current wheeze# 8.6 (266/3085) 1.663 (1.073–2.578)§ Cough¶ 18.0 (559/3097) 1.622 (1.162–2.266)ƒ Hay fever 11.7 (360/3082) 1.171 (0.756–1.814) Skin-prick test (≥3 mm) 19.4 (341/1762) 1.373 (0.857–2.200) Pollen 13.9 (243/1754) 1.567 (0.940–2.613)+ Specific IgE aeroallergens 36.3 (476/1311) 1.213 (0.755–1.947) (≥0.7 kU·mL-1) low: 2600–15000 vehicles·day-1; medium: 15001–30000 vehicles·day-1; high: >30000vehicles·day-1 in street segment <50 m away from home.#: with respective symptoms during the last 12 months;¶: morning cough during the last 12 months. ORs adjusted for age, sex, socioeconomicstatus, and family history of asthma, hay fever, or eczema.
  18. 18. Table 4: Respiratory and atopic outcomes in relation to traffic counts (High exposure tertile) in the area of residence outcome Crude reference prevalence % Adjusted OR (95% CI) (raw numbers) Asthma 10.4 (318/3071) 1.194 (0.762–1.871) Current asthma# 5.0 (157/3124) 1.790 (1.051–3.048)§ Current wheeze# 8.6 (266/3085) 1.663 (1.073–2.578)§ Cough¶ 18.0 (559/3097) 1.622 (1.162–2.266)ƒ Hay fever 11.7 (360/3082) 1.171 (0.756–1.814) OR: odds ratio; CI: confidence interval; Ig: immunoglobulin; Skin-prick test (≥3 mm) 19.4 (341/1762) 1.373 (0.857–2.200) Pollen 13.9 (243/1754) 1.567 (0.940–2.613)+ Specific IgE aeroallergens 36.3 (476/1311) 1.213 (0.755–1.947) low: 2600–15000 vehicles·day-1; (≥0.7 kU·mL-1) medium: 15001–30000 vehicles·day-1; high: >30000 vTable 5: Respiratory and atopic outcomes in relation to traffic counts (high exposure tertile) ehicles·day-1 in street segment <50in the area of residence for children additionally exposed to environmental tobacco smoke m away from home. #: with respective symptoms during outcome Crude reference prevalence % Adjusted OR (95% CI) (raw numbers) the last 12 months; ¶: morning cough during the last 12 Asthma 10.8 (126/1169) 1.343 (0.736–2.452) months. ORs adjusted for age, sex, Current asthma# 5.2 (62/1193) 2.047 (1.005–4.171)§ socioeconomic status, and family Current wheeze# 9.1 (107/1178) 1.697 (0.927–3.106)z history of asthma, hay fever, or Cough¶ 19.1 (226/1186) 1.543 (0.967–2.462)z eczema. Traffic categories Hay fever 10.4 (123/1179) 1.739 (0.967–3.126)z analysed versus rest of population (reference). Skin-prick test (≥3 mm) 15.8 (110/695) 2.670 (1.353–5.268)ƒ +: p=0.05–≤0.10; Pollen 11.8 (82/694) 3.255 (1.581–6.699)ƒ §: p=0.01–≤0.05; ƒ: p≤0.01. Specific IgE aeroallergens 33.1 (164/496) 1.761 (0.897–3.458) + (≥0.7 kU·mL-1)
  19. 19. Methods: Sample: 5338 school children (10.4 years) attending 108 randomly chosen schools in 6 French cities Exposure: concentrations of PM2.5 (fine particles with aerodynamic diameter p2.5 mm) assessed in proximity of their homes. range:1.6-54micm3, NO2 8.1-70.4 Health: asthma and allergy. Children underwent a medical visit including skin prick test (SPT) to common allergens, exercise- induced bronchial (EIB) reactivity and skin examination for flexural dermatitis. Their parents filled in a standardised health questionnaire. Confounders: sex, socioeconomic , passive smoking, family history of diseases, ethnic group, NO2.
  20. 20. Table 4: Odds ratios (95% confidence interval) of allergic and respiratory morbidity by high vs. low concentrations of PM2.5 and NO2 in all (n=5338) and long-term resident (n=1945) children of the French Six Cities StudyThe two categories of exposure „„low‟‟ vs. „„high‟‟ were defined with respect to the median value of the distribution of theconcentrations; EIB: exercise-induced bronchial hyperresponsiveness as assessed by PEFin–PEFfin/PEFin X10% (PEF ¼peak expiratory flow); SPT: skin prick tests. Odds-ratios (ORs) were adjusted for age, sex, family history of allergy and passivesmoking. a8 years at the same address.
  21. 21. 3. Annesi-Maesano et al 2007 (cont.)Table 5: Odds ratios (95% confidence interval) of allergic sensitisation by high vs. low concentrations of PM2.5 and NO2 in all (n=5338) and long-term resident (n=1945) children of the French Six Cities Study Odds-ratios (ORs) adjusted for: age, sex, family history of allergy and passive smoking. 2 categories of exposure „„low‟‟ vs. „„high‟‟ were defined with respect to the median value of the distribution of the concentrations; EIB: PEFin–PEFfin/PEFinX10%. a8 years at the same address.
  22. 22. Methods: Sample: 812 children from nine Dutch schools within 400 m of motorways. Exposure: Daily levels of PM10, obtained from background monitoring stations. Long-term exposure was assessed using specific traffic-related characteristics such as total, car and truck motorway traffic and the distances of the children’s homes and schools from the motorway. Health: Asthma and Allergies questionnaire, Offline exhaled NO measurements Confounders: sex, age, nationality, socioeconomic , passive smoking family history of diseases, etc.
  23. 23. H-Graveland et al 2007 Figure 2: Adjusted* geometric means ratios with 95% CIs for the associations between traffic characteristics and PM10 levels and exhaled NO in children with and without asthma. *All effects were adjusted for individual confounders (sex, age, current parental smoking, current pet possession, parental education level, non-Dutch nationality, gas cooking, parental allergy, presence of mould stains in the home) and downwind location. Effects of traffic characteristics were additionally adjusted for outdoor PM10 on the day of exhaled NO measurements; effects of PM10 were additionally adjusted for total traffic and distance of the school from the motorway.
  24. 24. 5. Dales et al 2009Methods: Sample: 182 elementary schoolchildren with physician-diagnosed asthma Exposure: city monitored ambient hourly air pollution concentrations. Health: morning and evening forced expiratory volume in 1 s (FEV1) for 28 consecutive days; daily symptom diary Confounders: sex, time of outdoor activity, temp., RH, week-day
  25. 25. 5. Dales et al 2009Mean (95% confidence interval) for diurnal change in forced expiratory volume in 1 s (FEV1)associated with interquartile increases of air pollutant concentrations averaged from 08:00 h to 20:00h on the test day.adjusted for daily mean temperature, relative humidity, day of the week and time for outdoor activity onthe same day and study period r.
  26. 26. Methods: Sample: 861 children with persistent asthma in 7 US urban communities Exposure: Daily pollution measurements from the Aerometric Information Retrieval Health: 2-week periods of twice-daily pulmonary function testing every 6 months for 2 years. Asthma symptom data were collected every 2 months Confounders: site, month, temperature
  27. 27. Table 3: Mean (95% CI) change in pulmonary function parameter at the 90th percentile of pollutant concentration relative to the 10th percentileTable 4: Risk of asthma-related symptoms and missed school days at the 90th percentile of pollutant concentrationrelative to the 10th percentileCovariates include site, month, site-by-month interaction, temperature, call number, and intervention group. Independent variable isthe 19-day average pollutant concentration.
  28. 28. Methods: Sample: 249 subjects (57 asthmatics, 192 nonasthmatics) age- high schools Exposure: BC and PM2.5 monitored continuously outside three NYC high schools and one suburban high school for 4–6 weeks Health: daily symptom data using diaries Confounders: school, weekend, and daily maximum 8-hr average O3.
  29. 29. Table 4: ORsa (95% CI) for respiratory symptoms and use of medication for asthma associated with an IQRbincrease in pollutant concentrations at various lags of exposure a Models combine data from all schools and adjust for school, weekend, and daily maximum 8-hr average O3. b IQRs are 1.2 ƒÊg/m3 for BC, 16 ppb for NO2, and 11.3 ƒÊg/m3 for PM2.5. c Sample sizes vary among pollutant models because of differing patterns of missing pollutant measurements. *
  30. 30. Methods: Sample: 53 subjects with asthma, 9-18 y in Los-Angeles Exposure: Personal hourly PM2.5 mass, 24-hr PM EC and OC, 24-hr NO2 and the same outdoor central-site exposure Health: Spirometry 10 days (*3) Confounders:
  31. 31. Figure 2: Adjusted single- and two-pollutant models Figure 3: Adjusted single- and two-pollutant models(coefficient and 95% CIs) for change in FEV1 in relation to (coefficient and 95% CIs) for change in FEV1 in relation topersonal 1-hr maximum PM2.5 the last 24 hr, and 2-day lag day 0 personal 24-hr average NO2 (pNO2) or PM2.5average NO2 measurements. (pPM2.5), with ambient 24-hr average NO2 (aNO2).Expected change in FEV1 corresponds to an IQR change Expected change in FEV1 corresponds to an IQR changein the air pollutant, and estimates are plotted by open in the air pollutant (Table 2), and estimates are plotted bysymbols for single-pollutant models and solid symbols for open symbols for single-pollutant models and solidmodels adjusting for the indicated co-pollutant. Single- symbols for models adjusting for the indicated co-pollutant.pollutant models are for the subset of nonmissing Single-pollutant models are for the subset of nonmissing
  32. 32. Methods: Sample: 45 grade children with asthma at four South Bronx schools (10 children per school) Exposure: Daily 24-hr personal samples of PM2.5, including the elemental carbon (EC) fraction during 1 month and outdoor… Health: Spirometry and symptom scores were recorded several times daily during weekdays
  33. 33. Table 2: Mixed model estimates of lung function decrements associated with personal and school-site pollutants a from 5th-95th percentile of pollutant concentration weekdays only, 9am-9am. b same day afternoon lung function measurements. * p-value < 0.10 by t-test
  34. 34. Figure 1: Relative risks of cough, wheeze, shortness of breath and Figure 2: Relative risks of cough, wheeze, shortness of breathtotal symptom severity scores associated with the various personal and total symptom severity scores associated with the school-and outdoor school-site particle and gas exposure measurements site integrated measurements of Sulfur, EC, and PM2.5.
  35. 35.  World-wide: Higher exposure to self-reported truck traffic on the street of residence is associated with increased reports of symptoms of asthma, rhinitis, and eczema in many locations in the world. In German children: High vehicle traffic was associated with asthma, cough and wheeze, and in children additionally exposed to environmental tobacco smoke, with allergic sensitisation. However, effects of socioeconomic factors associated with living close to busy roads cannot be ruled out. In the French 6C suffering from EIB (exercise-induced bronchial) and flexural dermatitis at the period of the survey, past year atopic asthma and SPT (skin- prick test) positivity to indoor allergens were significantly increased in residential settings with PM2.5 concentrations exceeding 10 mg/m3 (WHO air quality limit values). After adjustment for confounders and NO2 as a potential modifier The relationships were strengthened in long-term residents (>8 years). In Dutch children: Short-term (not long-term) changes in ambient PM10 largely attributable to biomass burning are associated with increased levels of exhaled NO (marker of airway inflammation)
  36. 36.  In Canadian children with asthma: Relatively low concentrations of urban air pollution worsen lung function over a short period of time, even within a day. (PM2.5 appears to be the most important pollutant). In US inner-city children with asthma: short-term increases in air pollutant concentrations below the National Ambient Air Quality Standards were associated with adverse respiratory health effects (reflected in pulmonary function) / absence from school . (The associations with NO2 suggest that motor vehicle emissions may be causing excess morbidity in this population). In US adolescents: Acute exposures to traffic-related pollutants- DEPs (diesel exhaust particles- a local driver of urban PM2.5); and/or NO2 may contribute to increased respiratory morbidity ; urban residents (compared with suburban) and asthmatics may be at increased risk. In NY-Bronx: Significantly elevated same-day relative risks of cough , wheeze ,shortness of breath and total symptoms were found with an increase in personal EC, but not with personal PM2.5 mass. Increased risk of cough and total symptoms was found with increased one-day lag and two- day average school-site. Adverse health associations were strongest with personal measures of EC exposure, suggesting that the diesel “soot” fraction of PM2.5 is most responsible for pollution-related asthma exacerbations among children living proximal to roadways. Studies that rely on exposure to particulate mass may underestimate PM health impacts.
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×