 please we're going to start again I just got the reason we I know it's a short time for coffee and sorry for that but we've got people online too following the following this school and so for all I mean for these people waiting for the the start it's important too for us to try to respect the times I know it's not that easy and Eric has got a word for you yes I have some other sides but if you okay so so there we have sent an Excel file the Excel file for the case study you have received it on your emails online and here it's ICTP so if you have a computer right now you can work here download the file and play with that file according to the case study here if you don't have a computer we have a computer lab just beside this room the you can go there and use the computer the password and login are on your bad that's the one you use for the Wi-Fi so feel free whenever you you need to go it's just next to the amphitheater I can show you the way if you want okay okay I made a correction to this right probably there is this overestimation but it's kind of is only an exercise so we can we can move on to the to the presentation about some some other issues before starting the exercise I put in in the in my presentation the the some some milestones about the the publication about the impact assessment like pollution in my knowledge this is the first publication in in Europe the for the outdoor and traffic related pollution assessment by Nino Kunze and colleagues in 2000 they in in this in this article they introduce the many of the concepts we are we have discussed in these days and also the the concept of the risk function the the slope of the of the relationship between air pollution and and and health outcomes and you can see there is no threshold and at our knowledge not threshold is defined and so regarding the discussion before this is an indication by this this work and the and the the choice of the counterfactual scenario are made in considering different level of exposure and the consideration of risk function for 10 for increase of 10 microgram per cubic meter of pollution after after this experience many other experience of impact assessment has been made the the the vias project that Carla show the I think the first day and in the meantime the other initiative in Italy the impact of Piantana zone and other pollutants in the main cities in Italy with the estimation of the mortality due to pollution to air pollution and another aspect that I want to introduce that is related also to the exercise is the the burden of disease the the concept that the rise when we have more than one and point and health and points and possibly more more than one exposure so we have to consider different health outcomes and how to bring together in the one number these different information so the concept of burden of disease that is the sum of the here of life lost so related to mortality due to premature mortality and when when we say premature mortality that is a mortality that arise before the expected life the life expectancy so the the the age at the which the the expected the life hand and the years lived with disability that is an indicator related to morbidity not to mortality but to other health outcomes related to hospital admission or other situation this is a general concept of a burden of disease that is related to every situation when we talk about air pollution or environmental stress so we focus on environmental burden disease again considering the concept of population attributable fractions so they not possibly not a population a population that is exposed to the environmental stress so this is a slide from our career out on in and that is a very very clear for me to understand the concept of values that is the measure that I think in in another letter will be addressed but the is a measure that can that allow us to consider together information about mortality estimation about mortality and estimation about morbidity that is a sum of indicator that are related to the the weight of disability and the duration of disability the weight of disability is often is usually a number that range from 0 to 1 1 is related to mortality to death so the case of mortality is is a weight equal to 1 and in the other case in the other situation of not mortality so the concept of year year with disability we have to consider the the length the duration of the these are of the health outcome and the weight in term of gravity so we can see here in this in this example that the asthma is a weight that is minor respect to pneumonia because pneumonia is a very very bad situation of health but with a little very little duration so you can you can think to the area of to the calculation of the area of all these situation and the the calculation is a multiple the multiplication by the disability weight by the duration so this is a way to put together different health outcomes in a unique value of course when we talk about mortality this the rectangle is cover all the ipsion axis because the weight is one with this approach there is a very very useful a very powerful study made by Otto Annen and colleagues to calculate environmental bargain of disease in Europe considering nine risk factors in six countries in six countries of Europe this is very very useful for different reasons the first is the the calculation and the the calculation of bargain of disease for different different environmental stressor that confirm that if we work on PM 2.5 we we consider a large part of of bargain of disease due to environmental stressors but also noise rather than dioxins and secondary and smoke is a relevant have a bargain of disease related to environmental stressors another another reason to consider this this study is related to the type of equation that are proposed in this in this work that that consider relative risk information concentration response function from epidemiological studies as the main as the gold standard if if there is the availability of this this information so for PM 2.5 for PM 10 for N02 for other pollutants this information is available but not this information is not available for all pollutants such as dioxins on red or I read on there is no concentration response function suggested by WHO on based on evidences so this approach provide a a useful way to consider also unit risk that are typical of auto psychology a typical of risk assessment and there are considered in in in a in a common way to provide a formula to for the calculation of attributable cases also when we have only the the information about unit risk unit risk and not a relative risk okay your internet connection okay this this was made on these six countries in Europe and there is there is an article published on environmental perspective but also a downloadable very detailed report that you can download from this website this is the table in the article that show the the large amount of stressors that are considered and you can see in what I said before you can see that they consider unit risk or relative risk considering relative risk where valuable otherwise unit risk and to to make calculation that also there is there are the references for this values and I don't have time to go through this approach but you can see that the first approach that it is the attributable fraction is the same that we have used for the exercise we used for our exercise based on risk as a relative risk so the epidemiological approach but in otherwise there are other three situations in which we can use again an epidemiological approach but with less availability of information about background incidence rate and to derive attributable cases in the case of availability only of unit risk this is a very very powerful very useful approach they provide the information about six countries together and also for each of the six countries in this slide I report the situation for Italy that almost the same of the six countries and also they derive the environmental disease in relation to the total value of disease and again you can see that Italy, the value of disease due to environmental stress in Italy is about six percent in Finland that is the range more from Finland that three percent to Italy that is six percent. There are other in the literature calculation of environmental burden of disease this is a slide from 2012 from a member where the WHO in which there is a disentangle, the different health outcomes are disentangled in terms of mortality and morbidity so considering the dialies as a unique indicator and you can see that moving from net arms to the other the percentage of environmental fraction is very different. I think that will be a lecture also for economic costs so this is a very powerful point of discussion from the, in particular for the discussion with the stakeholders and this was a first slide for a discussion of uncertainty but I don't think that we have more time to go into the this aspect but only to show that the wonderful work from Manin and colleagues also consider the team of the uncertainty and they assess a kind of uncertainty in their calculation. This type of uncertainty is related only to the part of the calculation so the statistical uncertainty, the model uncertainty, the uncertainty related to availability of data. There are other kinds of uncertainty but these are related to the quantitative assessment and you can see again that for particulate air pollution we are in a situation of evidence that is very robust. Okay now we go back to our example on land fields. We start yesterday, we move through the slide with some explanation and then I will leave 15, 20 minutes to complete the exercise. Yesterday we have considered how to calculate exposed people to define, if you remember, to retrieve information about the geolocation of the land fields, the DEA database, the DEA raster five for European population to derive population living in the buffer of four kilometers around the land fields, sorry. Today we will select, we will select the relative risk from literature, we calculate the attributable case with the formula I showed you before and we calculate, we will calculate Dali's because we will have to calculate more than one health outcomes and so to give, to provide a unique number, final number we will use Dali's. So this is our formula. We have to select the health outcomes and then the relative risk. The background population incidence, we have to retrieve this from a health for all the database and the exposed people we have discussed yesterday how to calculate. I will provide you the calculation of how many people is exposed to different land fields. What about Dali's? The calculation of Dali's is provided from starting from attributable cases multiplied by the two factors that for the rectangle that we have seen before in the slide. So the gravity, the weight of the disease and the duration. I provide in the slide the information about the weight and duration. This is derived from the Victorian burden of disease that is a common use database for calculation of Dali's and you can see in the first number is related to the disability weight and the second number if is reported is the duration. If there is not a second number, the expected duration is one because we are considering respiratory symptoms that we think that have no duration in time so we consider one as the length of the duration. We have considered also some health outcomes, some birth outcomes and so in this case the duration is equal to expected life expectancy. So at the time of this calculation the life expectancy was 79.6 years. We will not calculate annoyance in our exercise but annoyance is from odor that are related from the landfill and it will be a good point because there is no relative risk in the situation but information derives only from questionnaires to people living around the landfill and the sort of concentration response function is simply the percentage of people that reported annoyance from odor from landfill. So in our study we considered 5.4% of total population as annoyance. So one, one what? No, yes, yes. It's a good question, it's also a question that has a referee in our work. We have no information so we consider one related to other situation this is one year but it's an assumption. We have no information about this so we consider one. A suggestion of one referee was to consider kind of zero point because we have no, I don't think that respiratory symptoms, the duration of illness due to respiratory symptoms is one year but we chose this assumption in absence of information. Okay, this we have already seen this yesterday. Okay, for the background incidence rate we have used the health follow-up database. We have already talked about this and for the relative risk we have used the information for H-rapia if available. In the case of landfill we have to, we made an undocked review of evidence because H-rapia didn't provide information about relative risk function for landfills for this particular environmental stress so we made a review from literature and we found these four health outcomes to be considered. This is the 5.4 that we said before about annoyance from Hodor and in our exercise we will focus on these two health outcomes. There's a lower weight and respiratory disease. There are two main articles that provide these two relative risk, 1.06 and 1.09 from lower weight and respiratory disease so we will use this information for our exercise. This is again the data we put on the GIS. This is the health follow-up database. I think that is an older version that now there is the newer version that is the slide I showed you before but if you go through this database you can retrieve information very detailed in particular we calculate the background incidence rate for respiratory disease and lower weight and so you can go into this database for each country and you can obtain this type of information. So I have provided you a simple Excel file with I think 26 rows of one for each country with this information already provided in black that are the country name the name of the country the radius desired information that you don't need but just for information. The total population living within buffer that is the result of the the analysis I showed you yesterday about with the GIS information of a population and the EPRTR registry. The respiratory rate the background population respiratory disease rate and the percentage of low birth weight on total births these two information are provided by the health follow-up database. We have taken for this for if you see in the Excel file there is a different value for each country. The rate of births on total population again from a health follow-up database and the relative risk for the two outcomes that is 1.09 1.06 that I showed you before from literature. So the Excel file is filled with this information. What is the exercise is to complete the red part of the file with the calculation of at the end of the DAGIS due to these two health outcomes and to do this you have to make some step in the calculation that are the calculation of births starting from population and birth rate. The calculation of tributable fraction for respiratory disease attributable fraction from lower weight and the calculation of attributable cases for respiratory disease a lower weight and then the calculation of DAGIS. So these are the instruction for the exercise. My suggestion is to work because at the end when we go on the classroom at the end I will show the result for Italy. Not all. So my suggestion is to calculate for Italy and then calculate for each country you want but the minimum is the calculation for Italy. So work on Italy and another country row if you want. Create this new column calculate total births using information that are on the file. Calculate attributable fraction using relative risk column. This problem. Boris you wanted to know when do we start and when do we go to the room? So you have to give a go. So it's now? Yes, yes. I have one slide more. Okay, you can go to the exercise and if you want here there are the formulas but my suggestion is to try not to look at the formula and try to write the formula for yourself but if you need the here there is the solution in terms of formulas and so you can go to do the exercise. I'm saying let's say at least half an hour I mean you need sometimes to read. You think you need the formula or not. Okay. So if you need to go to the computer room meet me at the top of the amphitheater and let's go hack this. Work together is possible. I just sent an email. An email. Only got on your key. I'm sorry. I didn't see. HIA the only Excel file. We have three days on any folder new that we are going to create. But I didn't want to disturb them. You can put the switch on the video so they continue to see. They continue to see this. Perfect. Because if they are doing the exercise they don't have the formulas that they can do. And no, you see they are seeing everything. They don't see the formulas. Okay, let's start later. And now what? But they are seeing the shit. How many times do we have to go out? I don't know. Okay, even if there are other materials it's okay. I'll clean it myself. Anyway, in the key. I've been to the whole thing because it was an instruction. So I have to talk to my people. I guess you'll introduce yourself. It was just Isabella. No, no, no. No, well, it's clear. So, I think we can start. So maybe you want to talk right in the audience. Yeah. Yes. Eric. I mean, Eric, probably the questioner. No, I mean just to have a look. Just to have a look. No, I don't think we can. We can be from the camera and see the camera. We're not so good at video. No, no, you're fantastic. But you just don't want to be people. To have a look. Yeah, okay. We can do that. But the issue with this class is that people have to go over the right as it is. I didn't take the... I didn't say... That's what they get. So I think we have to... Maybe we should... We have to do that. Yeah, that's how to find Isabella. So let's meet up. We have two questions. At least we can. Isabella, let's leave him here. We have to go. Yes. One, two, three, four. Okay. I wanted to do that. I had no time. Okay. Just something. I'm going to say just to fill it and just to put it in somebody's mail. Okay. Tomorrow. Tomorrow. But we're going to name a sort. Yeah. Okay. At the right. I'm sure it's sort. What is that column that says next? That says yes. They just say yes. That's it. That's it. Thank you so much. That's it. That says... I expect that to open. Okay. Okay. Okay. This is the population. Okay. And the percentage of... Okay. So... Yes. Yes. Okay. Maybe you need to clarify that. It's clear in the variable here. Yes. The first column to the calculation of births is related to total population multiplied by births rate. There is a label that is expedited, expected the births. But it's the number of births. Okay. Yes. This is referring to the EXP column. Okay. EXP LBW. Okay. Okay. Yes. Okay. Yeah. Okay. Okay. Okay. Okay. Just a comment, we can do it together. We can do it together. Yeah. I have to choose between these two I can do it like this like this and now I have to do this but now I have to do this on the other side I can do it if I want to pay, I can do it or call I can do it I can do it and then I can choose and then I can choose and then I can choose are you are you ready guys so you need more time okay it five minutes at 12 the second I'm just looking at the year I'm really tired I'm not interested in everything I'm not interested in anything That's a little bit of a poor thing, but the beer is no longer available. In my opinion the beer is the best. This is mine. And I... You can spray it again. So you won't show it. Yeah. Yeah. Thanks a lot. This is our beer. We don't have a name. They want to have cookies. In Romania. They don't have a name. They don't have a name. They want to repass a new chef. No, not today. It depends on what day the teachers are going to... I have to be there, that's why I have to be there. No, not today. Today we have to discuss. Because we can decide. We have to support. So maybe two... They want to be exercised for the students. So we don't have to... Especially me and you. For example, for Andrea. In other instance, to be... Down there. Usually one person comes with a slide. Is it made? No, no, no. If we ask the teacher. How do you mind? Because they can send slides and comments. And you can talk. You can talk. And also if now... It's not just in Canada. When it's over, it's over. I can start? Yeah. Okay. So in 5-10 minutes we go to the solution of the exercise. Before showing the results. Again, we... I talk about the sources of this information. The first column of population, because someone asked me the sources of this data. The population is the population that is exposed to landfill size. So the population living within 4 km around each landfill for each country. So this is the result of the old work that I showed you yesterday about GIS, the overlapping of the layer of the geocoded landfill and the layer of the EEA population database raster file. The results for each country is the population. The respiratory rate and birth rate and the percentage of low birth weight are health data, sanitary data, and they are retrieved from the health forward database, selecting each country and going down through the list of health outcomes to derive respiratory rate, birth rate and so on. The last two values, 1.09, that is 9% increase of risk and 6% increase of risk for lower weight, are from the review of the literature we made within this study for health effects of the vicinity of... leaving the proximity of landfill size. It's okay, okay. We move on to the exercise. These are the formulas that I suggest you to use to make calculations. So the calculation of the birth rate, someone said that there was a wrong label in the beginning of the column, but it's the formula. We want to know from the total population, the birth rate, the number of births that is our population for the second outcome. The attributable fraction for respiratory disease for lower weight is relative risk minus 1 divided by the relative risk. So you have to take the relative risk for respiratory and lower weight. The calculation of attributable cases for respiratory and lower weight is the explanation of the formula, the three factors of the formula. So the attributable fraction times respiratory rate or respiratory times population in the second case, the percentage of lower weight divided by 100 because we have a percentage, multiplied by the population that is in the case of the birth outcome is births. And finally, we have to calculate the values. So the sum of two equations, the attributable cases times the weight of the disease times the duration of the disease for respiratory and lower weight with this coefficient. From Victorian burden of disease, we use the coefficient for duration and weight from the Victorian burden of disease that is used also by WHO for dialysis. So this is the results for Italy. If you have your calculation and you can check if the final number of dialysis is 1032, this is the calculation for Italy and then we could have a calculation for others. If I am able, I go to the Excel file that I made. Wow, okay. And here there are the formulas. Yeah, I don't know you. Zoom, zoom. Okay, here. I can see the formula. Yes, yes, you can see the formula. You see the formula on the top in this part. Okay, this explanation of the formulas that in the picture, in the slide, particularly for dialysis, is a sum of two different equations. It's okay for all or someone of you have wrong final results? Okay, oh, no, it's impossible. We made it together. Yes, good exercise. I think we just learned that some of the, or most of the parameters there can be obtained from the Victoria website or something, but clearly this will be for Europe and we are wondering where else we can get some data for Africa. Okay, okay. I have not the complete answer. A partial answer is to check in the WHO website about dialysis where are the complete sources for dialysis that they used. And I think that is the best choice you can do for your weights. It's a good observation. Maybe an additional remark is that if you correlate those dialysis with the total population, then you see that of course the total population is controlling the final result for the dialysis because it's an absolute number. Yes. Yes, and following this comment, I have to show that in the whole study we made, the bigger contribution to the dialysis derived from annoyance, from odor, because people exposed to annoyance is a very huge proportion of population. And so if you can see, if you made the sum of your Excel file, you find at least 13,000 that is the sum of these values, but you can see the number of the annoyance from odors that has very low, very little coefficient, duration one, very little coefficient, but a huge amount of population exposed. So at the end of our study, the conclusions are that annoyance is the main factor that provides the number of dialysis. So my question is, I found that dialysis is about 1,000. For me, it's information that I can extract from these values. And second, if I would like to normalize for 1,000 people, not all the 2 million people, only 1,000. No, 1,000 refers to the number of ears lived with disability. You live with disability. No, it's not referred to person. It refers to total number of ears that population in that country lived with disability. It could be 10, 100, 500 persons, but it's the sum of the number of ears with the problem caused by the pollution due to landfill. This is the explanation of dialysis. I mean, this means that over the 3 million people living around that landfill, those 3 million people, as I mean, 1,000 years will be lived with a certain disability. Is this the interpretation? In a sense, yes. I mean, just to understand what we are giving as a result. You have to consider also the duration of the life for this 3 million person that during the life of this 3 million person, there are 1,000 of ears of all these ears that are lived with disability due to presence of lens. Because we were discussing before, I mean, as you showed, for the different countries, we have different dailies. And for countries with very low population around the landfill, the dailies seem zero, zero point something. So it seems that there is no concern in that area. But if we divide those dailies for the population, as it was suggested before, then we have similar values. And so we can come... I mean, the question is, does this make a sense? Absolutely. Because if you divided there are different coefficients that are playing a role here. But considering the respiratory rate, birth rate similar, the percentage of low births similar, it's obvious that if you divide the dailies for the population, you have a similar number. Because this number could be interpreted as the number for each one of problems due to the presence of landfills, the presence of landfills. The number that we have to care for each person to the presence of landfills. The problem is, you are close to a landfill or you are not close to a landfill? In a country that the number, the number of dailies is close to zero, the possibility are true. They have no landfills or they have a good policy to locate landfills in the territory. I think. So if we think about Cyprus, the dailies is coming less than one. If you... Cyprus, for the country Cyprus, the dailies calculation is less than one. But while for UK, it is 3,894. So that means if we interpret this data, it means UK is very bad in condition. But if we normalize this, so we find for Cyprus it is 0.64. If we normalize the dailies with total population per thousand population, if we convert the data into a thousand population, so we find Cyprus is 0.64, the next country comes to Bulgaria, so 0.60. So I think this normalization will help us to make the country, make the data comparable. Yes. So that is our point. In the UK, there is a huge number of landfills. Huge numbers. So a very big number of people exposed. In Cyprus, one or two, there is a consideration. In the EPRTR file, there are stored landfills with a minimum amount of tons. So very little landfills are not reported in the EPRTR. So we discussed yesterday with three landfills in Cyprus. Yes. But only one is reported in the EPRTR because it is the biggest one. So very little population exposed. A huge number of population exposed in England, but if you divide it, because there is no valuable information, I think, that can help us to assess that in different countries the landfills have different health outcomes on population exposed. We have no information to disentangle this aspect. Is this okay with everybody? The time is running. Not strictly to the calculations, but about the interpretation of the results. Okay. We now, we are not going to prove any causal effect related to having landfills or incinerators in your country or close in the area that you are studying. For, if your hypothesis is that you are not going to this health impact approach, you need an environmental epidemiology. You're in this exercise, you are providing figures, data, proving the impact using the evidence of the health risk coming from other situations, the concentration response function that you decided to apply and to estimate the number if you are calculating the number of cases or the dailies or life you are lost, whatever indicators you choose to calculate the impact, how much, and how much in Bulgaria, how much in UK, so in order to compare them. If no people is resident close to the area, generally speaking, there is still an health effect of this plant because we know that from the literature, but in that case the impact can be zero, minus than zero because no one is exposed. So actually, if you have a plant, if you are talking, not an air pollution, if you shift to industrially contaminated sites or waste disposal sites and no one lives there, good. That's the proper place to put that farm that industry. So please don't take into account that we are not proving the effect of the exposure, but you are just quantifying the number of people affected in order to provide evidence-based decisions to decision-making. That's what health impact is for. Yes, and the final point that is interesting for the discussion in these days, this was a study made with colleagues from WHO, so it's an important exercise we made in condition of not optimal knowledge about concentration response function, exposed people, and kind of exposure. So it's a rough situation, yes, but it was useful to provide information about the impact of landfills. And this information was taken from colleagues from WHO and presented at the last environmental and data ministerial conference. So is a possible answer to the question, what can I do? Is it good to make an health impact assessment without a complete knowledge about the exposure, the effects and the population exposure in a sense, yes. It could be useful information to give to the stakeholders. Thank you very much, Andrea. I think it was very useful this exercise. Thanks for preparing. So our next speaker now this morning is Augustin Collette. He's from Inéris in France and also of Paris. Augustin is a specialist in modeling air quality in the past, present, and future, I would say. And he will give us a few elements about model...