PROBLEMS IN ENVIRONMENTAL SCIENCES AND ENGINEERING
PROBLEMS IN ENVIRONMENTAL SCIENCES AND ENGINEERING ENVR 890
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Date Created: 10/25/15
PublicPrivate Partnerships Takehome Lessons What is a PPP The combination of a public need with private capability and resources to create a market opportunity through which the public need is met and a pro t is made What are the steps in the PPP process 0 Catalyst initiates discussion 0 Formation of a steering committee Mobilization of funds Formative research 0 Design communications strategy 0 Test communications strategy 0 Implement interventions Monitor evaluate and adjust What is formative research 0 Every country has its own cultural norms and special circumstances 0 Understanding these factors allows tailored program design 0 Must assess problemneed factors enabling change and factors preventing change 0 Good formative research provides a solid foundation on which to build the framework of an intervention program What are some good things about PPPs Financial and inkind resources are contributed Local amp international efforts are combined Locals guide the development with expert aid 0 Efforts are focused on a circumscribed problem 0 Programs are compatible with the population 0 Education is a durable good What are some bad things about PPPs Selection of partners can be tricky Con icts of interest to ensure profit 0 Financial leverage affects decisionmaking Shifting of responsibilities from governments Sustainability is questionable Ethical considerations Bureaucracy Where can I learn more about the PPPs discussed in this presentation wwwglobalhandwashingorg Environ Sci Technol 2007 41 398473990 Predicted Secondary Organic Aerosol Concentrations lrom the 0xidation oi Isoprene in the Eastern United States TIMOTHY E LANE39 AND SPYROS N PANDIS39quot Department of Chemical Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 and Department of Chemical Engineering University of Patras Patra 26500 Greece Isoprene the most abundant nonmethane hydrocarbon emitted into the troposphere has generally not been considered a major source of SOA due to the relatively high volatility of its oxidation products In this study the SOA formed from the oxidation of isoprene is predicted using a threedimensional chemical transport model PMCAMx across the eastern US for July 0ctoberJanuary and April 200172002 The variability of the measured SOA yields in the available smog chamberstudies is captured by combining the base case scenario with upper and lower bound estimates ofthe measurements Forthe base case simulation the predicted annual average isoprene SOA concentration in the southeast is 009 ag m 3 bounds 0047023 ag m s Isoprene is predicted to produce 70 less SOA across the entire domain for spring and fall than during the summer and negligible amounts of SOA during the winter During the summer the average concentrations in the northeast are predicted to be 011 ag m 3 bounds 0047 031 ag m 3 and in the southeast0194g m 3 bounds 0117 058 ag m s PMCAMx predictions are compared to relative humidity seed loading NOX and 502 levels etc varying SOA yields were measured for different amounts of isoprene reacted Edney et a1 3 and Kliendeinst et a1 9 suggested that the isoprene SOA yields are higher when H2504 is present in the particulate phase The SOA mass yields from the oxidation of isoprene increased by a factor of14 from 02 to 28 in the presence of a strong acid 3 However the ambient aerosol in continental areas rarely contains pure H2504 due to the presence of NH3 11 Kroll et a1 6 7 reported that the SOA mass yields from the photooxidation of isoprene range from 09 to 55 in high NOX conditions Under low no NOX conditions Kroll et a1 7 measured SOA mass yields ranging from 09 to 36 Ng et a1 8 measured the realtime SOA formation during the photooxidation of 500 ppb of isoprene at a VOCNOX ratio of18 The nal mass yield at the end of the experiment was 2 assuming a density of 125 g cm a Dommen et a1 10 measured SOA mass yields as high as 5 and observed the formation of oligomers in the aerosol from isopreneNOX photooxidation Other studies suggest that isoprene can contribute to ambient SOA by heterogeneous reactions under acidic conditions 12 or through polymerization of second genera tion oxidation products 13 Through model simulations Ervens et a1 14 and Lim et al 15 suggested that cloud r 39 f l W 39 r quot 39 nmducts may also contribute to the formation of SOA According to Lim et a1 15 isoprene can contribute at least 16 Tg yr 1 of SOA on a global scale through cloud processing Isoprene may form organic aerosol through a number of pathways In this study the base case parametrization for the oxidation of isoprene uses the SOA mass yields and saturation concentrations proposed by Pandis et a1 5 together with the yield equation developed by Odum et a1 16Temperature dependence is added to the corresponding saturation concentrations to estimate the isoprene SOA T r 1 J r L available measurements of some isoprene SOA r in North Carolina and New York State These modeling results suggest that on an annual basis isoprene oxidation is a small but nonnegligible organic aerosol source in the eastern US lts contribution is relatively more important during the summer and in the southeast US 1 Introduction Several recent studies have suggested that isoprene may be an important source of SOA 1 3 Claeys et al 1 measured significant concentrations of tetrols in the Amazon and provided strong evidence at these low vapor pressure compounds were formed during the atmospheric oxidation of isoprene 2 3 With a global emission rate of 500 Tg yr l isoprene may produce appreciable amounts of SOA even with low SOA yields 4 Clayes et a1 1 estimated an annual SOA global source strength of 2 Tg yr 1 from the oxidation of isoprene Recent studies suggest that isoprene has SOA mass yields ranging between 0 and 5 depending on the amount of isoprene reacted 3 510 The chamber studies of Pandis et a1 5 Edney et a1 3 Kroll et a1 6 Kroll et al 7 Ng et al 8 Kliendeinst et al Correspondingauthorphone 41226873531 fax 41226877139 ermail spyrosandrew cmue u 39 Carnegie Mellon University 1 University of Patras 3984 l ENVIRONMENTAL SCIENCE 8t TECHNOLOGY VOL 41 NO 11 2007 case parametrization to predict the isoprene SOA concentra tions dependent on whether the primary organic aerosol FDA is included in the organic solution 16 19 Given the u concentrations The lower bound parametrization uses the base case parametrization at 30 C without a temperature dependence and without the FDA included in the organic solution quot buundis calculated 39 t t 2 SOA mass yield For each scenario a threedimensional chemical transport model PMCAMx is use to pre ict the isoprene SOA concentrations across the eastern United States for four different simulationperiods luly 1228 2001 October 1 17 2001 January 117 2002 andApril 117 2002 Thepredicted base case isoprene SOA concentrations with the FDA included in the organic solution are compared to available ambient tetrol concentrations measured in Research Triangle Park NC 20 and Potsdam NY 21 The predicted upper and lower bound isoprene SOA concentrations give an estimate 0 e r is ene SOA 39 the eastern US for each seasonAlthough there are signi cant J 39 quot 39 ene SOA concentrations the seasonal variations forisoprene SOA across the eastern US are quantified and discussed rr 101021e506 l3 l2q CCC 3700 2007 American Chemical Society Pu 39 bllshed on Web 04242007 0045 004 l SOA Mass Viclds 0045 004 0033 3 0 ll 02 A Mass Yields 0 w 0015 001 0005 0l 1 I0 100 l 000 Aerosol Concentration llg m39l Fl URE 1 Measured and predicted isoprene SOA yields E versus total organic aerosol n5 o1 isoprene reacted Predicted SOAvields using the base case para l 0 150 lsuprcnc chctcd ppb 200 concentration and lbl veisus atmospheric metrilation at 10 C 20 C and 30 C Measured SPA yields are lioin m iel 5 ll iel 5 way D iel 7 w iel 3 they 0 iel y o iel m and iel 4with m and without loiay Al so2 assuinino g cm a density ol 10 2 SIJA Modeling T e chemical reaction pathways for atmospheric voc oxidation are complex and the resulting oxidation products h numerous and difficult to measure Laboratory ganic aerosol formation from tory experiments is de ned as c yicf In order to solve for the aerosol concentrations ofn species the above equations ined 39 39th n to yield a system of n equations wi se oxida ion of specific v s have mostly measured the total 532115 s yie The fractional yield 1 of SOA produced MW from the oxidation of a voc is defined as i fox 1 1 n 5 Y M 1 AVOC wheteMo It HI I I from the reaction ofAVOC pg m 3 of gaseous voc M r r a multiplerptoduct model using the following equation 1 ZMD whete X is the mass based stoichiomettic coefficient fol uIation concenttatio l S 0 1 Moff an individual VOC can be modeled by assuming the formation of a the mass balance WMWL AVOC MWVOC ELL Egi L aem 3 where cu cm and cm are the total gasrphase and aetosolr 39 39 39r n ti it anrlmw and MerC are the molecular weights of compound i39 and the voc The gasrphase concentrations will satisfy cg yxc fotzi 4 where x is the mole fraction of compound i39 in the organic solution of is the saturation concentration of pure 239 and y is the activity coef cient of the species 139 The effective satuIation concentration which is detetmined from laboras i A Md avetage mo leculat weight When the POA is not included in L 39 39 L 39 L is set to zeIo fot the aeIosol calculations Within the pattitioning model the Cla siusrClapeyton equation is used for t e effective satuIation concentIation to account fol changes in e peratur 2 ase case parametrization uses the Pandis et al 5 yields and saturation concentrations in eq 2 The SOA yields are 00107 0 0036 and 0 007 w39 73 404 and 1928 rg m j res ctively The molecular weight and enthalpy of vaporization for all three products were set at g molquot 73 kJ molquot 22 With enthalpy of 427 156 kl Fi iire mass ss vaporization for SOA species ranging from molquot theselected 7s 1d molquot isoprene reacted to measured SOA yields from refs 5 3 and H 0 assuming a density of 10 cm j The SOA yields from i the et al 9 measured the SOA mass yields at 25 c While the base case parametrization does not match exactly any of tln tnrli 39 39 39 39 across the wide range of conditions used To capture the variability oftheseresultswe rely on upperand lowerbound scenarios proximate lower bound was set as the base case p 39 at 30 c without temperature dependence Figure 1b At atmospheric levels of isoprene reacted the a SOA mass yields as m asurements o mmen et al 10 performed at 20 Figure 1 lfless than 140 ppb of iso prene are reacted in the VOL 01 NO 112007ENVlRONMENTAL SClENCE ampTECHNOLOGv3985 laboratory the lowerbound expressionpredicts an SOA mass yield of zero However in the atmosphere a fraction of the corresponding semivolatile vapors will dissolve in the existing organic aerosol phase giving nonzero yields at all reacted isoprene levels For the calculation of the lower bound we assume that the produced SOA will form a solution with the other SOA compounds but not with the primary organic aerosol The upper bound which is set as a constant 2 SOA mass yield is around the maximum SOA mass yields measured by Kroll et al 6 This is consistent with the maximum SOA mass yield predicted by the base case parametrization at very high values of reacted isoprene or existing organic aerosol Excluding the SOA yields measured by Kroll et al 7 the maximum SOA yield that has been measured at Tern Ieti39 J 00p 39 39 2 Figure 1 The upper boundwillpredicthigher SOAyields than measured by Kroll et al 7 at low organic aerosol concentrations less than 2 ug m a For this scenario the mpounds are assumed to have zero vapor pressure at all temperatures The results of this upper bound scenario can be easily scaled to different values of the maximum constant yield For example if one assumes a constant yield of 4 instead of the 2 used here the corresponding concentrations can be calculated by multiplying our upper bound results by a factor of 2 3 PMCAMX PMCAMx is a threedimensional chemical transport model which uses the framework of CAMx 24 to simulate horizontal and vertical advection horizontal and vertical dispersion wet and dry deposition and gasphase chemistry Three aerosol modules have been implemented in PMCAMx to describe 39 39 J 39 J 39 J cloud 39 39 r t 39 25 PMCAMxtracks 13 different aerosol species sulfate nitrate ammonium aerosol water content four secondary organic aerosol species sodium chloride primary organic aerosol primary elemental carbon and all primary inert material The size distribution of each aerosol species has ten size sections ranging from 40 nm to 40 um For this study PMCAMx is applied to four 17dayperiods in the easternUS starting on Iuly 12 2001 October 12001 lanuary 1 2002 and April 1 2002 The modeling domain covers a 3492 x 3240 km region in the eastern US with 36 x 36 km grid resolution with 14 vertical levels up to 6 km 25 Inputs to the model include horizontal wind compo nents temperature pressure water vapor vertical diffusivity clouds and rainfall all created using the meteorological model MM5 25 26 The chemistry mechanism used in PMCAMx is the Carbon Bond mechanism version 4 24 27 The LADCO BaseE inventory 24 is used for the emission inputs for PMCAMx According to the emission inventory the highest average rene emission rate is 70 kg km Z day1 during Iuly in southeastern Oklahoma Ianuary has the lowest maximum emission rate 5 kg km Z day l April and October have 39 39 39 withthe 39 39 39 rate near 20 kg km Z dafl For all periods the isoprene emission rates are highest in the southeastern US Isoprene is only emitted during the daytime when sunlight is present Lane et al 29 Gaydos et al 25 and Karydis et al 30 J J L across the eastern US to measured organic carbon con centrations from the EPA Speciation Trends Network STN and the Interagency Monitoring of Protected Visual Environ ments IMPROVE During the Iuly simulation period the mass for the months ofOctober Ianuary andApril compared to STN and IMPROVE measurements is 017 092 and 027 Mg m a During these periods the model is overpredicting on average the organic aerosol concentrations 4 Results and Discussion Across the entire modeling domain the average primary organic aerosol POA concentration at the ground level during the 17day Iuly 2001 simulation period is 11 Mg m 3 29 The domain average groundlevel POA concentrations during the 17day October Ianuary and April simulations are higher than for the Iuly simulation at 13 ug m a 16 ug m a and 15 ug m a respectively With the addition of the POA to the organic solution more organic aerosol is present to absorb the secondary condensable products For Iuly the predicted domain average total secondary organic aerosol SOA concentration at the ground level is 07 Mg m 3 29 30 The maximum predicted average total SOA concentration of 25 Mg m 3 occurs above southern Arkansas for the Iuly simulation In the southeast the total SOA concentrations are dominated by biogenic SOA which has a similar spatial distribution to the isoprene SOA The r 39 V 39 r quot Lohavehigh contributions around northern cities For October Ianuary and April the domain average total SOA concentrations are 04 02 and 04 Mg m 3 30 4 l SOA Produced from lsoprene Base Case Scenario The average predicted concentrations of SOA from the oxidation of isoprene using the base case parametrization with the POA included in the organic solution for Iuly October Ianuary and April are shown in Figure 2 The monthly and annual average isoprene SOA concentrations in the northeast and southeast are listed in Table 1 In the southeast isoprene is predicted to contribute an average of 009 Mg m 3 bounds 004023 ug m a of SOA annually using the base case In the northeast the predicted isoprene SOA concentrations are around 50 ess than in the southeast The annual average predicted isoprene SOA concentration in the northeast for the base case scenario is 005 Mg m 3 001011 ug m a The average isoprene SOA concentration is predicted to reach its maximum during the summer During the Iuly simulation the predicted average isoprene SOA concentra tions are 019 Mg m 3 01 1058ug m a in the southeast and 011 Mg m 3 004031 ug m a in the northeast using the L 39 larv quot i innme SOA concentrations in both the northeast and southeast are near zero 001 Mg m a The April and October simulations are predicted to have average isoprene SOA concentrations ower than in Iuly but higher than in I anuary The average isoprene SOA concentrations for October and April are 008 Mg m 3 bounds 002018 ug m a and 006 Mg m 3 001 014 Mg m a in the southeast and 003 Mg m 3 001006ug m a and 006 Mg m 3 001009 ug m a in the northeast respectively On an annual average basis the concentrations of the isoprene SOA represent a few percent of the total organic aerosol concentration These concentrations are small but nonnegligible The wintertime values are predicted to be 20 times smaller than those during the summer Isoprene SOA during the summer in the southeastern US is predicted to contribute 7 of the total organic aerosol The addition of the isoprene SOA during the summer to the organic aerosol predicted by PMCAMximproves the agreement ofthe model with the available measurements by correcting the small tendency of the model to underpredict the summertime SOA aerosollevels with a mean bias ofi02ug m a 30According to Karydis et al 30 the mean bias for predicted total organic 3986 l ENVIRONMENTAL SCIENCE amp TECHNOLOGYVOL 41 NO 11 2007 e absolute mean bias between the pre dicted total organic mass and the IMPROVE and STN measurements was reduced by 01 ug m a FIGURE Z Average SOAl 39 0r uxil 39 39 lictl 39 ll during a July 1220 2001 lb October 117 2001 El January 117 2002 and d April 117 2002 IAElE 1 Average Predicted lIlllllelle SIJA Concentrations all in ill the Southeast and Northeast SIlIulornairls lor Jll 12720 2001 etoller 1717 2001 January 1717 200 April1717 2002 and for the Year scenarin sulnlnrnain luly Octnher January April annual SE 019 0 08 001 0 base case NE 011 0 03 000 0 05 005 base case SE 015 0 05 001 0 03 006 wo POA NE 007 0 02 000 003 003 lower 011 0 02 000 001 004 bound NE 004 0 01 000 001 001 pper 0 18 002 014 023 bound NE 031 0 06 001 009 011 ingthePanrli alal and 023 pg m 3 in the southeast Henze and Seinfeld 31 used a was roduct model fit for the low NO SOA ields measured by Kroll et al a in a global model and predicted the yearly average SOA concentrations According to Figure 1 from e and Seinfeld 31 the SOA concentration increased by approximatel m 3 in the northeaste an m 39n I und predictions This agreement also confirms that our upperbound gives results over this area that are similarwith those of the Kroll et al a parametrization For the lower bound scenario the maximum average isoprene SOA concentrations for July Octoberlanuary and A quot n a 3 005 lpgm39 0 4 2 Upper and Lower Bountk of Isoprene SOA Figure 3 shows that the predicted isoprene SOA concentrations from the lower and up erbound scenarios have a similar spatial distribution as shown in the base case scenario for all simulation 39ods The upperbound scenario as expected predicts the highest isoprene SOA concentrations for each simulation period July being the highest with an average m maximumsimulated average conoentrationin this upper 11 pg m j over southern bound isoprene SOA concentrations for April and October are comparable r and 0 5 m j respectively For July the lowerbound average isoprene 3 concentrations in the northeast and southeast are 004 p m 3 an pg m irespectively Overall thelowerbonnd scenario only predicts signi cant isoprene SOA during July in the sontheastem US ct of Primar Organic Aerosol Using the base case parametrizationwithout the F0Ainclnded in the organic luti n 39 39 39 i m 3 over southern Arkansas during July 12728 2001With the rima organicaerosoladded to the organic solution the predicted isoprene SOA oversouthernArkansas increases to 036 pg m i However the inclusion of FOA in for October and April are 033 pg m j east of Atlanta on the GeorgiaSouth Carolina border 3 west 0 and 028 p m Little Rock AR respectively Figure 3 For January the maxi um avera isoprene SOA concentra m tion predicted for the upper bound scenario is only 005 pg m 3 in the upperbound scenario are 011 pg m 3 in the northeast The average isoprene SOA concentrations for the base case with and without the FOA included in the organic solution are 011 and 007 In the northeast and 019 and 015 in the southeast respectively Table 1 For0ctober and April concentration from thebase case scenario and the base case VOL 41 NO 11 2007ENVlRONMENTAL SClENCE ampTECHNOLOGv3987 FIGURE 3 The in lower and 2 upper bound average SOA concentiatiuns him the oxidation ul isupvene piedicted in the eastern us during a July 12720 2001 h Onuhev 1717 2001 c January 1717 2002 and d April 1717 2002 please nutethe diiievem scale used d in the July upper huun l m 3 and 006 POA to the 01 anic solution main In Dark Niquot Th Tmquot m j tespeotively With the addition of the avetage houtly isoptene SOA oonoenttations between the the do 39 39 39 39 SOA oonoenttations fol all foul simulation petio ds inotease 39 h the latgest effects in the utban ateas 39ons Varlauon Figute 4a shows the base case avetage daily isoptene SOA oonoenttations avetaged oveI 3988 ENVlRONMENTAL SClENCE ampTECHNOLOGVVOL M NO 11 2007 in Figute 4b In genetal the gtound level isoptene SOA concenttation is pledicted to dectease from midnight until sunrise The isoptene SOA oonoenttations statt inoteasing at some point afleI sumise Janna 7 am pIi 7 a Octobet 7 a pm and July 7 7 am Duting Aptil July and 0 7n Juy a Willloul no b 4 J18 Oclohcr r 39 ll POA Mum 050 en ti l6 pm 3 1 quotSH 39 UM m2 5 E E 040 2 n H g a 6 U will V 03930 lt s 8 two 3 mu 2 2 ll 04 g i V E 5 e e V 339 0 3quot 102 39 I 100 OUU 2 a o 8 IO 12 14 to ix 2 22 24 U 2 4 n it H i2 14 in IR 2021 4 Simulation Hnlu39 July Simulation Hour FIGUREA 39 39 I I 7M1 anuiurnn 4 each season over Research Triangle Park Nc 39 h 39 Summer Fall Wimer Spring 10 am befole increasing again in the aftemoon These a olsdamlV changes are due to the combined effects of photochemical 0 production during the day changes in partitioning due to temperature changes dilutionettecn and removal processes The maximum isoprene SOA concentrations are predicted to occur in the late aflemo nand early evening The diffelent scenarios all predicted similar trends throughout the day fol each location 45 Comparison with Obs the minimum 25th p maximum daily average isoprene SOA concentrations re cas erva ons Figure 5 compares ea c 6 Q Spring ac ion o concentration levels lltourtchev et al 32 found that tetrols represented approximately 50 ofthe as d39 opr roducts during the summer inab forest l lyytiala Finland However this fame of a low estimate o the isoprene so belo zem r the tetrols repr sent practicallyall oftheisoptene som uythenthe predict e mo re consistent t the ambie t conditions or pathways egaque0usrphase into account additional chemical production production need to be taken dam NY the observed t ion wa lower than the obse ed average summer concentration 21 The average predicted base caseisoprene SOA concentration by PMCAMx in this area in the fall was Wu thy Research innnglc I ark NL 00 on on 500lt 4m 4 mm g i T LT I I B 10 7 1 l J gig E acr zgtact amok v a Q a q ct it a rug Qquot 06 9 6 egg 9 0 9 a of 3 t 6 FIGURE 5 Comparison oithe minimum 25th percenti n 75th percentile and maximum pl I 39 Conccnlrmion ng m emea eo39cteo daily average Isupvene SOA concentrations trom the base case enario with the tetrol measurements trom tai rel 21 over Potsdam NV and hi rel znover Research Triangle Park NC 40 less than the average summer concentration In North Carolina the ratio of the average observed fall and spring leltol concentlalions 0 the summel concentlation ate 03 andn r9 nnrli a m 39 39 the conesponding PMCAMx ratios in that area are around r these seasons This indicates t at the summer concentrations may be underpredicted in that area or the VOL M NO 11 2007ENV RONMENTAL sclENcE ampTECHNOLOGv3989 fall and spring may be overpredicted Another potential explanation is that the ratio of the tetrols to the total isoprene 39s not constant during the year These curnpari on should be viewed as preliminary because they do not even refer to the same year Therefore yeartoyear differences 39 L quot 39 Additinnal en 01 concentration measurements are necessary together with a better understanding of their contribution to the total isoprene SOA mass concentration Acknowledgments The authors would like to thank the Lake Michigan Air Directors Consortium forproviding the meteorological les the area andpoint emission files and the initial and boundary condition les for PMCAMx and P Hopke and U Bal Center for Environmental Research NCER under grant R832162 This paper has not been subject to EPA s required peer and policy review and therefore does not necessarily re ect the views of the Agency No official endorsement should be inferred Literature Cited 1 Claeys M Graham B Vas G Wang W Vermeylen R Pashynska V Cafmeyer 1 Guyon PAndreae M OArtaxo P Maen aut W Formation of secon ary organic aerosols through photooxidation of isoprene Science 2004 303 1173 1176 E Claeys M Wang W Ion A C Kourchew 1 Gelencser A Maenhaut W Formation of secondary organic aerosols fro with hydrogen peroxride Atmos Environ 2004 38 409374098 E ney E O Kleindienst T E 1a0ui M Lewandowski M Offenberg 1 H Wang W Claeys M Formation of27methyl tetrols and 27methylglyceric acid in secondary organic aerosol 39 39 39 1 SO Q alrml hire an 39 n United States Atmos Environ 2005 39 528175289 GuentherA Hewitt C Erickson D Fall R Geron C Graedel T Harley P Klinger L Lerdau M McKay W Piersce T Scholes B Steinbrecher R Ta amraju R Tay or 1 Zimr merman PA global model ofnatural volatile organic compound emissions J Geophys Res 1995 100 887378892 Pandis S N Paulson S E Seinfeld 1 H Flagan R CAerosol formation in the photooxidation of isoprene and rpinene Atmos Environ 1991 25A 99771008 Kroll 1 H Ng N L Murphy S M Flagan R C Seinfeld 1 H Secondary organic aerosol formation from isoprene phor tooxidation under higheNOx conditions Geophys Res Lett 2005 32 L18808 Kroll 1 H Ng N L Murphy S M Flagan R C Seinfeld 1 H Secondary organic aerosol formation from isoprene phor tooxidation Environ Sci Technol 2006 40 186971877 Ng N L Kroll H Keywood M D Bahreini R Varutbangkul Flagan R C Seinfeld 1 H Lee A oldstein A H Contribution of first versus secondrgeneration products to secon ary organic aerosols formed in the oxidation ofbiogenic hydrocarbons Environ Sci Technol 2006 40 228372297 Kleindienst T E Edney E O Lewandowski M Offenberg 1 H Iaoui M Secondary organic carbon and aerosol yields from the irradiations of isoprene and ovpinene in the presence of NOx an S02 Environ Sci Technol 2006 40 380773812 Dommen1MetzgerA Duplissy1 Kalberer M Alfarra M R Gascho A Weingartner E Prevot A S H Verheggen B Baltensperger U Laboratory observation of oligomers in the aerosol from isopreneNCx photooxidation Geophys Res Lett 2006 33 doi 1010292006GL026523 Takahama Si Wittig A E Vayenas D V Davidson C 1 Pandis S N Modeling the diurnal variation of nitrate during the Pittsburgh Air Quality Study J Geophys Res 2004 109 D1650617D1650610 E Q 3 Q Q 8 3990 l ENVIRONMENTAL SCIENCE 81 TECHNOLOGYVOL 41 NO 11 2007 12 8 8 Q B Q 8 Q 3 Q 8 26 Limbeck A Kulmala M Puxbaum H Secondary organic 39 39 39 39 Geophys Res Lett 2003 30 1996 doi1010292003G1017738 Kalberer M P sen D Sax M Steinbacher M Dommen 1 Prevot A S H Fisseha R Weingartner E Frankevich V ennhi R39 U 39 39 39 39 39 r 39 39 39 Q 1 7 90M 0 1659 1662 Ervens B Feingold G Frost G 1 Kreidenweis S M A modeling study of aqueous production of dicarboxylic acids 10hemixm 39 39 39 J Geophys Res 2004 109 D15205 doi10102920631D004387 Lim H71 CarltonA G Turpin B 1 Isoprene forms secondary 39 nmce ing39 39 39 Environ Sci Technol 2005 39 444174446 Odum 1 R Hoffmann T Bowman F Collins D Flagan R F 39Semfeld 1 H aerosol yields Environ Sci Technol 1996 30 258072585 Odum 1 R 1ungkamp T Grif n R 1 Forstner H Flagan R C Seinfeld 1 H Aromatics reformulated gasoline and atmospheric organic aerosol formation Environ Sci Technol 1997 31 18901897 Bowman F Odum 1 Pandis S N Seinfeld1 H Mathematical model for gasrparticle partitioning ofsecondary organic aerosol Atm s Environ 1997 31 3921 931 Strader R Lurmann F Pandis S N Evaluation of secondary organic aerosol formation in winter Atmos Environ 1999 33 48494863 KleindienstT EdneyE Lewandowski MOffenberg11aoui M Seinfeld 1 Format wn mechanisms for secondary organic aerosol in ambient air European Aerosol Conference Ghent Belgium 2005 Xia X Hopke P K Seasonal variation of 27methyltetrols in ambient air samples Environ Sci Technol 2006 40 6934 6937 Sheehan P E Bowman F M Estimated effects oftemperature on sec ndary organic aerosol concentrations Environ Sci Technol 2001 35 21292135 Takekawa H 39noura H Yamazaki S Temperature depenr dence of secondary organic aerosol formation by photor oxidation of hydrocarbons Atmos Environ 2003 37 3413 3424 Environ User39s guide to the comprehensive air quality model with extensions CAMx Vers wn 410 Report prepared by NVIRON International Corporation Novato CA 2003 Gaydos T M Pinder R W Koo B Fahey K M Pandis S N J chemical transport model PMCAMx Atmos Environ 2007 4 4 259 2 Grell G A Dudhia 1 Stauffer D R A Description of the fifthrgeneration Penn StateNCAR Mesoscale Model MM5 1995 NCARTNV398STR httpzlwwwmmmucaredumm5l documentslmm57descrdochtml accessed March 2007 Gery M W Whitten G Z Killus 1 P Dodge M C A photochemical kinetics mechanism for urban and regional sca e computer modeling Geophys Res 1989 94 9257956 LADCO BaseEModelingInventory Midwest Regional Planning Organization Des Plaines 1 September 16 2003 httpl www1adcoorgtechemisBaseEbaseEreportpdf accessed March 2007 Lane T E Pinder R W Shrivastava M Robinson A L Pandis S N Source contr39butions to primary organic aerosol Com parison of the results of a sourcerresolved model and the m P Pandis t reerdimenslonal Chemical Transport Model PMCAMx in Eastern Un39ted States for all four seasons Geophys es press Henze D K Seinfeld 1 H Global secondary organic aerosol from isoprene oxidation Geophys Res Lett 2006 33 109812 doi1010292006G1025976 Kourtchev 1 RuuskanenT Maenhaut W Kulmala M Claeys L u c A f products of isoprene in boreal forest aerosols from Hyytiala Finland Atmos Chem Phys 2005 5 276172770 Received for review June 1 2006 Revised manuscript re ceived October 16 2006 Accepted February 27 2007 E5061312Q Figure 31 Classifier Shown with lmpactor Installed on lnlet Impaclion Nozzle or Jet Stream Lines Impaction Plate Figure 32 CrossSectional View of an Inertial lmpaetor Hinds 1982 r The impaction plate de ects the ow to form a 90 bend in the streamlines Particles with suf cient inertia are unable to follow the streamlines and impact on the plate Smaller particles follow Theory of Operation L39 8 3 Polydisperse AerosolIn quot Heat u Exchanger j 39 Temp r Aerosol Neutralizer Im actor Shfith lotirn39leter Polydisperse gt Aerosol Inlet Absolute 39 Pressure quot9 399 9quot O O O O O O 029202029201 gt Exhaust Monodlsperse f Aerosol Out 39039 a I 39gt 4 n o Bypass Figure 33 Flow Schematic for the Electrostatic Classifier with LDMA 0 Theory of Operation B S 100 o 1039 o a F N quot4 02 y erLQ 39 NCharge 0 N positive 39 lt 393 o N negative 01 1 10 100 am 1000 V 999375 39 Table B 1 Midpoint Mobilities Midpoint Particle Diameters and Fraction of Total Particle Concentration that Carries 1 2 3 4 5 and 6 Elementary Charges as a Function of Mobility Fraction of Total Particle Mobility Particle Concentration That Carries This Midpoint Diameter Number 16 of PositiveCharges cmzlvs Midpoint pm 1 2 3 4 5 6 2077E2 0010 0041 1 0 0 O 0 0 1 69915 2 001 1 00460 0 O 0 0 0 1390E 2 0012 00514 0 0 0 0 0 1138E2 0014 00573 0 0 0 0 0 9307E3 0015 00638 0 0 0 0 0 7615E3 39 0017 00709 0 0 O 0 0 6230 E3 0018 00784 0 0 0 0 0 5097E3 0021 00866 000012 0 r 0 0 O 4171E3 0023 00953 000023 0 0 0 0 3412E3 0025 01041 39 000041 0 0 0 0 2792E3 0028 01 145 00007 0 0 0 0 2284E3 0031 01241 0001 1 0 0 0 O 1869E 3 0035 01343 00018 0 0 0 0 1529E3 0038 01446 00027 0 0 0 0 1251E3 0043 01550 00040 0 O 0 0 1024E 3 0048 01650 00056 0 0 0 0 8375E4 0053 01748 00078 0 0 0 0 6853E4 0059 01841 00105 0 0 0 O 5607E4 0066 01925 00138 0 0 0 0 4587E4 0074 02000 00180 000045 0 0 0 3753E4 0083 02064 00225 000089 0 0 0 3071E 4 0093 021 13 00279 00016 0 0 0 2512E4 0104 02148 00339 00028 0 0 0 2056E 4 01 17 02167 00406 00046 000014 0 0 1682E4 0132 02167 00477 00072 000031 0 0 1376E4 0150 02 149 00552 00105 000064 0 0 1126E 4 0170 021 13 00627 00149 00012 0 0 9212E5 0193 02059 00700 00200 00022 000014 0 7537E 5 0221 01989 00768 00261 00037 000032 0 6167E5 0253 01903 00826 00327 00059 000068 0 5045E5 0291 01804 00871 00400 00088 00013 000013 4128E5 0337 01700 00900 00467 00125 00024 000032 3378E5 0391 01583 00910 00533 00168 00040 00007 2763E5 0457 01467 00902 00593 00217 00062 00014 2261E5 0535 01356 00879 00641 00266 00089 00024 1850 E5 0631 01251 00844 00678 00315 00122 00040 1514E5 0746 01 158 00806 00701 00360 00158 00060 1238E 5 0886 01081 00770 00701 00400 00194 00084 1013E 5 1056 01023 00749 00703 00422 00227 001 10 The formulas used to calculate Table B1 are shown below They are taken from Wtcdensohlcr 1988 To calculate the fraction of particles carrying zero one or two charges use Equation B2 7 y Theory of Operation 8 9 0P7 10w Omfj g CamilaS l I v 39 View volume 4 x V Isensor chamber Jl 39 I I r x a t o gt Cone 1quot quot h I C gt x Mirror Lamp Photomulciplier L g Lquot Pm 7561c fm Calibracar Clma O Dflco aeroso comfey o 3 Scum 39 64710 03am are 5 foo macx of ceacdealvomi Ma a217M swu lem ANNULAR APERTURE SIGNAL HIM APERTUIE O V so BEAM W IMAGING SYSTEM I l H 7 FROM LASER I l PARTICLE PLAN 6256 opfm ewo cow171PMS 05 042 He Me Mmeam ase 0 304am LIqH S LH m5 WNW 07 see a s 3 97 5 MSW71 ode 1m Malawi mam s j SCGlflrlnj amp 537 x g A 1 5 d Pat w6W ll an A 2 5 Maw 774 4 47 may SM4Jm4 ZPS A at 3 5114 5k 7 W a s v I 37 0140 tax K e ak94 Sea mr 3560hajw L HH 7 2de m39 u o ax40w mpm czu 7 14 390 7 0 Wire W7 sadMW WWW W quot7 7 mec ggfz r AS ce c MA 354 ax2y Io a It MilanJ Mina 039quot Z r I 0mm 1 MewMW l L IL 1 0 7 42 pdlld Ianm oJL 2 I 0 1L 39 i 5 A grg l am I mquot faquot5Q J a g KW a 396 WIWS39f 0 87739 l out My 09 hdtxrftmc7ll Scal39lleri j v3 Relative scattering Mie intensity parameter E 2 1000 111M111 O 1 10 10 100 Size parameter a quot k l l 0 17 17 17 Particle diameter for A 052 urn for DC A03902am S ZOIHVL 3I4l COUNTER RESPONSE 390 T llrllll lllll Illlll llll 1 REFRACTIVE E INDEX E 39 ml54005i I 39 HOW I l OJ 1 II 1111111 I Jilllll DJ IO 20 PARTICLE DIAMETER Lm Figure 2 Theoretical response of the Bausch and Lomb 401A particle counter adapred from Cooke lo I I Illllll l I1llllll I l I g 39339 34 o gt I oquot a U 2 g 3 o O T O E 2 L BAUSCH a LOMB quot PC 40l 00 I l I IIIIII I I I lIIIIl I I I OJ I I0 50 Particle Diameter pm Figure 3 Experimental calibration curve for the Bausch amp Lomb 401A particle counter Op 71161 p HIIDClC 6001716 cdz mlp39n VOLTAGE PULSE AMPLITUDE VOLTS 3 IllfFIT FjlllllL an ax I MEL FACTORY CALIBRATION cuava quot O POLYSTYRENE LATEX PARTICLES 3 a GOP PARTICLES quot 002 001 1 lLLLlLll L Illllll 01 02 0304 06081 2 3 4 a e 10 PARTICLE DIAMETER MICRONS Figure 5 Voltage pulse amplitude vs particle size for the Climet 208 counter DEC 10 1987 Day 2 39 n 16 cooking a H E 12 H 3 F D u E a 9 08 J 04 u avg od99J 013973 0274 0447 o21 113918 1324 239 028 024 046 070 094 1 69 2 36 particle Diameter um r H I 3933 M 39 6x l I I K W mmmwfwmww gt9 AM3 lt oc 3 igeg 2 39 I Vm w o 3 39a g g t g quot 39 123352332 ia fgql 2A 9 Q m lt quot 3 g uMmes Hulllml lll lt Geog L 6QA gt a 0 39 A M 0 Jquot 20 40 Pm 5 5W g CQ l 39 a rofmm W 1 W 90 39 23 M US WM 7ampme Oafa I 1 i Electron Microscopy i Principles and Techniques for Biologists j John J Bozzola PhD MS Center for Electron M z39croxcopy Soutbem Illinois Univem39ty C orbomz ale Illinoisquot MS Labofdtofj Structural Biology w r Soutbem Illinoir Umbem39ty Scbool Magicinequot z 39 C arbamz ok lllihoz if 39 TEM SEM i i i Elgctron l earn I l 11 E7 w 39 l 39 Signal39 l J From Specimen Viewing Transduced Screen Specimen Two Basic Types of Microscopes JONES AND BARTLETT PUBLISHERS 51 BOSTON 39 quot 39 Specirnen Preparation for Scanning Electron Microscopy 43 SEM SPECIMEN PREPARATION BIOLOGICAL I BACTERIA VIRUS ETC NON BIOLOGICAL 39 PLANT ANIMAL ETC BULK SPECIMEN TISSUE EXCISED AND TRIMMED CENTRIFUGATION EXPOSE SURFACE TO BE VIEWED X39RAY X39RAY BY CLEANING POLISHING FRACTURING WASH WASH SPECIMEN ETC g AIR DRY FIX I 39 FIX 2 GLUI ARALDENYDE 00 FIXATION I SPECIMEN I TO VACUUM PEI pv b SIZE A oo 39 4 3 25 503025 6 IOUu 6 cm DEHYDRATION FREEIE maven J IJ J FREEZE DRYING I I I V c 15 507 757 9571007 TRANSITION FLUID I lt MOUNT ON STUB WITH ADHESIVE SUBSTANCE SPECIMEN NON CONDUCTIVE CRITICAL POINT DRYING CARBON ARC TUNGSTEN r BASKET 391 CONDUCTIVE SPECIMENS To TO PUMPS TRANSFORMER CARBON FOR XRAY PdAhL FOR SEM IMAGING COATING BY EVAPORATION SEMxRAY OR SPUTTER METAL COATING Figure 31 Schematic showing sechncc of events for processing biological specimens for SEM Courtesy of Judy Murphy V Scam 4 E cvlrm Microscopy Scamhf Vivc same4y agoms m 71 fyIMd 7fo SPWWM 766 Swamiay defmm m Clo011157 147440014 m7 fu a 7 03744635 oa qm Marm camspr o r u mm isemeary KM1m wiAaA are Malia47 7 4 pang Mc Mad7 cWa mnyy ws as 7 cm Ziam Swap am 7 44 SWMZ 5 M annlo V e quot a M110 llact w focus mm 4 lm Sigma HEWHm V 039 lam SpoHW Loahhy 39 Nogpggmxgnve r g h gals I5 MadamInfo 9 RC 7 44 MMMV a my agMay 39 1mg 1 A pacMm 4 lm ol d 102m aAu V y 47 39 I SPECIMEN f I out w mgMM 9 5 if a 7 4 10 lpJ ps ztrecf u Pddf0l 539 T f J CARBON FOR XRAY PdAu FOR SEM IMAGING 39 M VIA4 Ja V COATING BY EVAPORATION aam 539 MW 7 39 on SPUTTER METAL COATING r W cad1 g 5066 max 5w ace Alia Khan M39smm Jennifer Espiritu Leslie Arney Tina Lusk cuminn r em w 39 VIUT 35 x r m l r Kc it D a ll K 7 3953 5 9 79 7 131 134 E39J yl U H M Q 7 3 Global Warming temperature increases from emissions of C02 and other greenhouse gasses IPCC report results unequivocal that Earth s climate is warming IPCC report very likely that emissions from anthropogenic causes are responsible for most of the observed 7 increases in average global temperatures in 20th century y I39 5 Y l IPCC 2007 WHO estimates that climate changes since 1970 s could have led to over 150000 deaths annually and 5 million DALYs each year Patz and Olson 2006 Burden of Disease in the Developing Underdeveloped World WHO s assessment furthermore revealed that the poorer regions of the world are most vulnerable ibai rmine w Extreme High Temperature and its effects on Public Health Foodagriculture I 39 Water Resources Sanitation HeatWaves e Atmusphen infrared Suunder AiRS instrument abnard NASA SA iasateiiite sensestemperature using infrared waveiengtne Ann2003 State of the Agricultural Sector 3th 39 Wquot MI W M39 4 According to the USDA food security has improved globally lead to decline in total of people wo access to adequate food 1 real price of food grains greatly improved the food security of the majority of the world39s poor who spend a large share of their incomes on food grains Global number however masks variation in food security among regions countries and social groups that are vulnerable because of low incomes or a lack of access to food 2001 IPCC 531 State of the Global Agricultural SectorhttpWMNgridanocimateipcctarng208htm531 Agriculture Direct Effects of 1 002 on Food RmeGmm Increase firmness Decrease iron and zinc levels of rice Combined 1temp and CO2 lowers protein content VVheat reduces the protein content of grain and flour by 913 2001 IPCC 531 State of the Global Agricultural Sectorhttpwwwgridanoclimateipcctarng208htm531 Agriculture World Food Prospects Optmistic Population growth expected to decline in 21st century Evidence suggests that agricultural productivity potential is likely to continue to increase Not so optimistic Evidence that the Asian rice monoculture may be reaching productivity limits because of adverse impacts on soils and water Arguments that downward trends in food prices and other areas could have been misinterpreted 2001 IPCC 531 State of the Global Agricultural http nrida quot quot htm53 l The health impacts of drought on populations occur primarily via impacts on food production Famine often occurs when a preexisting g situation of malnutrition worsens The health consequences of drought include diseases resulting from malnutrition In times of shortage water is used for cooking rather than hygiene In particular this increases the risk of diarrheal diseases as a result of fecal contamination and waterwashed diseases eg trachoma scabies Outbreaks of malaria can occur during droughts as a result of changes in vector breeding sites Bouma and van der Kaay 1996 Malnutrition also increases susceptibility to infection 2001 IPC 953 Droughts http1lwwwgridanoolimateipootarwg2355htm953 Lack of Water Several indicators of water resource stress Amount of water available per person Ratio of water drawn to potential water available Withdrawals greater than 20 of total renewable resources water stress limits development Withdrawals of 40 or more represents high stress Country has less than 1700 m3 yr 1 of water per capita But indicators also depend on how water is managed 2001 IPCC 452 Impacts of Climate Change on Water Resources A Global Perspectivehttpwwwgridanoclimateipcctarng l 80htm452 Waiter QCQFCMquot Ever H me Na onal wa1er resources per capila m3 yr1 in 1990 and 2050 under several climate change scenarios for some countries Amell 2000 1990 39 39 39 and short black bars 2050 under different climate change scenarios I in the frequency and intensity of heat waves Warmer summers and milderwinters Humidity Much bigger health impact in cities than in surrounding suburban and rural areas Urban areas typically experience higher a d nocturnally sustained temperatures because of the quotheat islandquot effect Air pollution also is typically higher in urban areas nd eleva ed pollution levels o en accompany heat waves 2mm lPCC a A l HeatWavEsrhttp MNva grida rimIllmatElpccitavaQZBEB mmwal Excess summer mortality attributable to climate change assuming acclimatization was estimated to be 500 1000 for New York and 100250 for Detroit by 2050 Medium level of certainty assigned to this result Mortality from thermal stress in developing country cities may be significant Populations in developing countries eg in Mexico City New Delhi Jakarta may be especially vulnerable because they lack the resources to adapt to heat waves But most research refers to developed countries there has been relatively little research in other populations 2001 IPCC 941 Heat Waveshttpwwwgridanoclimateipcctarng353htm941 QQS H matce van39 ab y effem hwman mgch w nerabn w Flooding Over the next 100 years flooding is predicted to increase in frequency and intensity especially in low lyingcoastal areas and zones those that experience high rainfall TemporaryPermanent Flooding in areas at or below sea level Flood risks due to climate change have environmental technical and social factors which make determining specific locations at risk difficult Direct injury During storm Indirect Afterstorm returning home Cleaning up debris Post traumatic stress 4 7 x 7 7 7 Property loss and social disruption could cause the risk of mental health problems such as depression fir Hmpaclg on do Decrease potable Water supply Disrupt sewage lines releasing microbes into drinking Water sources Possible contamination of t s ormWater out ows could users Potential increase of c tospori 39um an campylobacter levels in Water Micropamsites which cause diarrheal diseases Immediate effects Drowning or being swept against h ard objects Medium effects Increase in respiratory diseases due to overcrowded shelters Increase in communicabledirraheal diseases due to ingestion of r contaminated wate Hepatitis A cholera Other fecaloral transmission Infection due to helminthes transmission in soil Vector borne disease Rodent Borne disease Long term effects Mental illness decreased economic and sanitation conditions Coping mechanisms apply at all levels of the hazard management cycle Mitigation preparedness emergency response and recovery These emergency response systems are generally less developed In developing countries Lack the resourcesfunds to develop effective systems Health related management includes Action in the home and community Storing water above floodlevel to avoid contamination Health and hygiene education How to effectively ration a limited supply of water without decreasing sanitary standards Warning and evacuation Educating the community on response plans Disease surveillance and control Healthcare provision Protection of health infrastructure Water sanitation protection TyndallCentreforClimateCquot g quot quot2ht tpAAAAAtvndallar How are we exacerbating the problem Upland forests can soak up a lot of water Deforestation due to development increasing hazardous flood risks to homes and people Wetlands can also soak up a lot of moisture Many are drained to make room for development Again increase in flood risks The Water Page httpwwwafricanwaterorgclimatechfactsheet13htm The spe factor in determining the number of immediate floodrelated deaths Generally weak data on nondrowning non immediate ood related deaths Infectious disease outbreaks often follow oods varying in magnitude and morbidity 0 Evidence from data in India and Bangladesh that rates of diarrheal disease increase after flooding 0 Due to contamination of potable water 0 Decrease in availability of clean water therefore less is used for hygienesanitationgt increase spread of disease Tyndall Centre for Ciimate Change Research A quot Changing precipitation patterns will affect how much water can be captured Several models suggest that downpours will become more intense This would increase floods and runoff while reducing the ability of water to infiltrate the soil Changes in seasonal patterns may affect the regional distribution of both ground and surface water supplies UNEP httpwwwafricanwat erorgclimatechfactsheet13htm Glacial melting may lead to flooding followed by droughts in coming decades The Himalayas contain the largest store of water outside the polar ice caps and feed 7 major Asian rivers The glaciers which regulate the water supply to the Ganges lndus Bra hmaputra Mekong Thanlwin Yangtze and Yellow rivers are believed to be retreating at a rate of about 1015m 3349ft each year Hundreds of millions of people throughout China and the Indian subcontinent most of whom live far from the Himalayas rely on water supplied from these rivers Andes Mountains Peru Glacial retreat accelerates sevenfold The edge of the Qori Kalis glacier was retreating 13 feet 40 m annually between 1963 and 1978 By 1995 the rate had stepped up to 99 feet 301 m per year Venezuela Disappearing glaciers Of six glaciers in the Venezuelan Andes in 1972 only 2 remain scientists predict that these will be gone within the next 10 years Glaciers in the mountains of Colombia Ecuador and Peru show similar rapid rates of retreat Temperature records in other regions of the Andes show a significant warming of about 06 F 033 C per decade since the mid19705 Andes Mountains Columbia Diseasecarrying mosquitoes spreading Aedes aegypti mosquitoes that can carry dengue and yellow fever viruses were previously limited to 3300 feet 1006 m but recently appeared at 7200 feet 2195 m Global sea levels have risen 1025cm in the last 100 years lnundation of coastal areas increased cost to protect those areas Migration of disease carrying vectors further inland Increased competition for land gt increased competition forjobs Increased competition for water resources gt future political conflict Western US Loss of habitat and species Increased contact between animalshumans as ecological niches are destroyed by floodingrising sea levels Poieniial impact of sealevel rise on Bangladesh Today Total population 112 Million Total land area 34000 km 15 m lm Toial populalinn mama 17 Mllllon 15 Tonal land area alfened 22000 km 16 mimm AnnaI 1 i 15 meter rise in sea level could displace over 17 million people in Bangladesh h UN Environment Program httpwwwgridanoclimatevital33 tm Effects of Climate Change on Infectious Disease Key Concepts Climate is a key factor in the geographical distribution and range of many infectious diseases Weather affects the characteristics of disease outbreaks timing and intensity Diseases of Interest Vectorborne diseases Malaria and Dengue endemic to more than 100 countries represent the greatest burden to human health among all vectorborne diseases Others Yellow Fever West Nile Virus encephalitis Waterrelated diseases Cholera and Schistosomiasis Others Cryptosporidium Giardia Vectorborne diseases Changes in temperature affect the breeding maturation and survival of vectors which lead to changes in their geographic distribution Increases in the average temperature and levels of precipitation has already facilitated spread Incidence also depends on relationships among hosts pathogens and vectors Hueiting Tsai 2005 Malaria Pathogen Plasmodium falciparum P vivax P ovale P malariae Vector Anopheles mosquito Hosts female anopheles mosquito and humans Breeding sites water Climate determines prevalence ofdisease geographical distribution and seasonality oftransmission Location Tropical and sub tropical areas Sub Saharan Africa South and Southeast Asia Temperature key determinant Effective malaria transmission gt20 P falciparum cannot complete its g Rainfall Increases in average precipitation andor flooding produces standing water creates additional breeding sites any dmm In mosquitoes In gut wall Sporoioites we on an nocyst Spuruzuites migrate in salivary glands Lquot 1 quot LTTW J a g 39 39 7 g 1 Ir mquot r 3 uh 1 2quot 392 4 1 139 injected 9 with 39 Liver stage IJI I humans ig jc sx PRIME 13 E Ezra 30 gt s P517 In Pix 1 it t ii H r U y itw mosquito s rate of reproduction 9increased density of vector 9 increased biting activity 9 increased transmission rate at which parasites mature 9shorterextrinsic incubation period 9 increased transmission At 20 C P falciparum take 26 days to incubate At 25 C 13 days Denduc l i Pathogen Flavivirus l 7 Vector Aedes aegypti mosquito l Location Yearround transmission in tropical subtropical areas Seasonal peak during months of high rainfall and humidity Increased temperature Mosquito larvae develop faster Faster maturation 9more adults capable of transmission Viruses have shorter extrinsic incubation periods virus can cycle more rapidly in the mosquito increase speed of epidemic spread 11 1x gt cf 41 EH it 61 77 D rquot i 1 fit Virgil i I c l 397 LA J E lt21 3 Extreme weather can have a great impact on water sanitation and hygiene Droughts Water scarcity often results in the use of poorer quality sources of water decrease efficiency of sewage systems Floods Excessive precipitation can result in contamination of drinking water sources caused by destruction of sewage treatment plants Ex Heavy rainfall events in the UK and United States have triggered outbreaks of cryptosporidiosis and giardia Ex 19971998 El Ni o excessive flooding caused cholera epidemics in Djibouti Somalia Kenya Tanzania and Mozambique WHO 1998b Lisle and Rose 1995 Atherholt et a 1998 Rose eta 2000 Curriero eta 2001 Cholera Bacteria Vibrio Cholerae Potential reservoir copepod zooplankton found in some shellfish Temperature dictates seasonality and geographic distribution Tropical areas year round cases reported Temperate areas cases only reported in warmest season also may be linked to Seasonality of plankton blooms Caused by warming of sea surface temperature Colwell 1996 Schistosomiasis Pathogen trematode Schistosoma Host water snails Prevalent in Irrigation systems hot climates where snail populations can survive and parasite can find human parasite carriers Effect of climate change water shortages 9 more irrigation systems and increases in host snail populations trematode egg production increases at high temps Schorr et a 1984 Conclusion Regions with greatest burden of climatesensitive diseases have lowest capacity to adapt Effects reflected in food and water resources and sanitation Infrastructures already operating at capacity Disease burden composed of usual players respiratory infections vector borne diseases diarrhea coupled with worsening state of health due to even worse than baseline malnutrition and collapse of already tenuous infrastructures Conclusion Immediate effects in the developing under developed world will have longer reaching consequences over time Sources International Panel on Climate Change 2007 Summary last updated 06 April 2007 last accessed 23 April 2007 at wwwipccch International Panel on Climate Change 2001 Report hitp wwwgridanoclimateip Few Ro er Floods Health and Climate Change a Strategic Review Tyndall Centre for Climate C ange Research2004 November 41 Patz JA Olson SH quotClimate change and health global to local in uences on disease risk Ann Trop Med Parasitol 2006 JulSep1005653549 Review Rose Joan B Climate Variability and Change in the United States Potential Impacts on Water and Foodborne Diseases Caused by Microbiologic Agents Schiermeier39Q The costs of global warming Nature 2006 Jan 2643970753745 Shope Robert Global Climate Change and Infectious Diseases Environmental Health Perspectives Vol 96 pp 171174 1991 Smith HV Sporulation of Cyclospora sp Oocysts Appl Environ Microbiol Apr 1997 16311632 Vol 63 No 4 Copyright 1997American Society for Microbiology Water Polic International Limited The Water Page 20001 Last accessed 23 April 2007 at mp lwwwafrlcanwaterorgclrmatech fact sheet13htm In class discussion for Thursday February 22 Please read the following paper Characterisation of Indicator Organisms and Pathogens in Domestic Greywater for Recycling Birks and Hill 2007 Environ Monit Assess While reading the paper consider the following questions 1 Where was the study done Do you think that the location could affect the results of the study List two factors that would affect the total quantity of pathogens found in the results 2 What pathogens do the authors find in the greywater sampling How many samples does the study test for pathogens How can the sampling affect the number of pathogens detected How well do the results for pathogens correspond with indicator microorganisms The authors list factors that could explain the differences in the results List two factors they mention that may account for the differences between the two results pathogens and indicators Does their reasoning agree with the material we discussed about microorganism survival in the environment Give one example from class lecture that might explain the results they found L V 4 Do the results suggest the water is safe Could the information provided here be used to assess the risk from exposure to the greywater
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