An introduction to Biometeorology and its application in regards to asthma. It includes also, future ideas to be implemented along this line. Three consecutive years of asthma information and weather information are correlated together in order to find possible indicators to define an asthma index.

1. Bio-meteorology of Asthma David Quesada School of Science, Technology and Engineering Management, St. Thomas University, Miami Gardens FL 33054

3. Why Asthma? Motivation of the study.

4. Previous results within continental USA and Miami Dade.

5. WeatherBug Mesonet and Asthma – Weather connection.

6. Statistical Processing

7. Minimal Bio-Physical model: Thermoregulation & Immunology

12. The spatial and temporal scales of various weather phenomena Characteristic length L – defines the spatial range for a particular event Characteristic time T – defines the time interval for a particular event to occur Ratios = L / Lc or T / Tc When numerical values of ratios are becoming large enough, then processes occurring at scales of the order of Lc (Tc) are averaged and appear as fixed for scales larger than those previously analyzed. “Long” Waves weeks to months Temporal Scales High / Low Pressure “Short” Waves Hurricanes Tropical Storms Mesoscale Convective Systems days to weeks hours to days Land / Sea Breezes Thunderstorms minutes to hours Tornadoes Small – Scale Motions (Turbulence) seconds to minutes 0.000001 km 1 km 10 km 100 km 1000 km 10000 km Microscale Mesoscale Synoptic Scale

13. Forms of Energy Transfer Conduction:Particle by particle transfer of thermal and electric energy. Heat transferred in this fashion always flows from warmer to colder regions. Generally, the greater the Temperature difference, the more rapid the heat transfer. Convection:Transfer of thermal energy by mass movement of a fluid. In a convective circulation the warm, rising air cools. In our atmosphere, any air that rises will expand and cool, and any air that sinks is compressed and warm. Advection:The horizontally moving part of The circulation (called winds) carries properties Of the air in that particular area with it. Radiation:Transfer of Electromagnetic energy through empty space in form of waves, traveling at a constant speed – c.

14. Composition of the Atmosphere

15. World Physical Geography and Climatic Zones UTC or Z – time = Universal standard Time = It is the time measured at Royal Observatory in Greenwich. EDT = Eastern Day Time = UTC - 5 hr (4 hr during time adjustment)

16. Slopes, Trigonometric Functions, Average Values, and Global Warming Range of variation Cloudiness and Random Fluctuations in the weather are responsible for theseirregularities It is worth to notice the periodicity (24 hrs) of these peaks; however it is clear the irregular shape of all these peaks too – Why?

17. Eagle Valley HS, Eagle Bend, MN Boyd Buchanan, Chattanooga, TN St. Thomas University, Miami Gardens, FL

18. Periodic Patterns in Nature and its Graphical Representation Time Series Analysis Daily variations – Days and Nights Period = T = 24 hr Daily, monthly, and yearly variations - three periods T1= 24 hr, T2= 90 days, T3= 365 days Maximum Mean or Average Minimum Range More complicated behaviors are indicators of hidden dynamical processes to be studied

19. Slopes, Trigonometric Functions, Average Values, and Global Warming Trigonometric Interpolation Case 1: The free term To is a constant Case 2: The free term To is a linear function of time Case 3: The free term To is a quadratic function of time Weather is all about the values of these Functions at some moments of time, known as the time series Climate is all about the value of this Integral, known as the average value

20. Slopes, Trigonometric Functions, Average Values, and Global Warming It is worth to notice how the trigonometric function oscillates around the main value function To(t). A minimum of 30 years it is needed to make a conclusion about a warming Climate. It is worth to notice also how , short Cold intervals may coexist with a warming trend.

21. Why to study Asthma? Asthma Statistics Worldwide Number of people diagnosed: more than 150 M Europe: the # of cases has doubled USA: the number of cases has increased more than 60% India: between 15 and 20 M Africa: between 11 and 18% population Number of deaths yearly: around 180,000 Miami Dade County , Florida 7.1% Middle and HS children were reported with asthma The number of hospitalizations due to asthma has doubled. The number 1 cause of school absences and 35 % of parents missed work

22. Why to study Asthma? How far Bio-Meteorology may help with?

23. Why to study Asthma? How far Bio-Meteorology may help with?

24. Why to study Asthma? How far Bio-Meteorology may help with?

26. Larger seasonal variation associated with a decrease in age. Aichatou Hassane, UNH; Robert Woodward, PhD, UNH; Ross Gittell, PhD, UNH - May 27, 2004

29. Create a database of weather parameters and environmental triggers for asthma ( WeatherBug & WeatherBug Achieve)

30. Zip codes patients came from WeatherBug Mesonet stations NWS stations, MIA & Tamiami

31. Seasonal Variations of Asthma diagnosed cases Kendall Medical Group in Miami Dade, FL

32. Seasonal Variations of Asthma diagnosed cases in standard units Z = (N – Nave)/S, Kendall Medical Group in Miami Dade, FL

33. Tmax Tmin Tmean=(Tmax+Tmin)/2

34. ΔT=Tmax-Tmin ΔT/Tmean

35. Pmean Pmax Pmin dPmean/dt

36. Hmax Hmin Hmean dHmean/dt = H[i+1] - H[i]

38. Correlations between the number of cases and the given set of variables (IBM-SPSS-19)

42. 90th Annual Meeting of AMS, Atlanta 2010

44. 91thAnnual Meeting of AMS, Seattle 2011

45. Future Directions Statistical Correlations --- Asthma Index --- Urban Biometeorology --- Experiment and Modeling --- Lung Dynamics and Immune Response --- Modeling --- Intelligent Expert System for Asthma Risk Analysis --- IESARA --- Online Weather Center --- Live Weather & Biometeorology ---

46. Statistical Analysis

47. Urban fluid dynamics and weather Weather Research and Forecast - WRF Microscale and Mesoscale meteorology

48. Air Quality Modeling and Human Health

49. Mathematical modeling of the episodes of Asthma by using dynamical systems and Complexity theory Macroscopic Dynamics of breathing Theory of Systems and System Biology Mesoscopic Immune cells population dynamics Microscopic Genes Cooperative effect – Emergent properties

50. Mathematical Biology: Mathematical Modeling in Physiology and Anatomy The dark outline on the left is an actual tracing of human bronchi, the schematic on the right is a computer-generated fractal representation. Measured across mammalian diversity, lung surface tends to scale to the 3/4 power of mass. Systems and networks that grow by iterative fractal branching exhibit developmental plasticity that allometrically scaling structures lack, and allow tissues and organs to respond adaptively to unusual circumstances.

51. Computational Fluid Dynamics and the dynamics of breathing Fractal branching along with the finite element method to re-create the airway mesh

53. Mesoscopic immune description of an asthma episode A system of differential equations describes the population dynamics of each one of the cells involved in an asthma episode. In asthmatic individuals, antigen presentation is thought to results in the polarization of T-cells towards a Th2 patterns whereas T cells from non atopic, non-asthmatic individuals show the opposing Th1 (interferon-γ and IL2) pattern of cytokine secretion A very complicated Network of cells (IL4, IL3, IL5, IL13- Cytokines, IgE – Immunoglobuline)Interacting and Competing.

54. Minimal model describing the functioning of the Immune system Logistic growth terms Non – linear dynamics

55. Intelligence Expert System for Asthma Risk Analysis - IESARA Geographic Information Systems -- GIS Artificial Intelligence Optimization

58. School of Science, St. Thomas University