WEBVTT 00:00:01.640 --> 00:00:04.040 The following content is provided under a Creative 00:00:04.040 --> 00:00:05.580 Commons license. 00:00:05.580 --> 00:00:07.880 Your support will help MIT OpenCourseWare 00:00:07.880 --> 00:00:12.270 continue to offer high quality educational resources for free. 00:00:12.270 --> 00:00:14.870 To make a donation or view additional materials 00:00:14.870 --> 00:00:18.830 from hundreds of MIT courses, visit MIT OpenCourseWare 00:00:18.830 --> 00:00:20.000 at ocw.mit.edu. 00:00:22.850 --> 00:00:25.790 RAJESH: My name is Rajesh. 00:00:25.790 --> 00:00:28.430 I am one of the co-founders of ClimateX, 00:00:28.430 --> 00:00:31.670 which is some of the groups who are sponsoring this event. 00:00:31.670 --> 00:00:36.650 We are really, really happy to have a wonderful cast 00:00:36.650 --> 00:00:40.510 of speakers, who will be talking to us about how to turn some 00:00:40.510 --> 00:00:43.820 of the work that we have all done in the field 00:00:43.820 --> 00:00:50.850 into advocacy, especially three kinds of advocacy. 00:00:50.850 --> 00:00:55.040 One is a citizen advocacy, which Nathan and Audrey 00:00:55.040 --> 00:00:56.390 will be talking about. 00:00:56.390 --> 00:00:59.540 Then there is legal advocacy, which is Chris. 00:00:59.540 --> 00:01:05.660 And advocacy by creating market solutions, which will be Ory. 00:01:05.660 --> 00:01:08.030 So I'm going to get out-- 00:01:08.030 --> 00:01:13.100 to prove that I'm not Donald Trump, I'll stop talking now 00:01:13.100 --> 00:01:16.830 and give it over to Chris, who is 00:01:16.830 --> 00:01:21.270 going to talk a little bit about how he, as an MIT chemical 00:01:21.270 --> 00:01:26.799 engineer, became a lawyer and is now going after the bad guys. 00:01:26.799 --> 00:01:28.840 CHRIS NIDEL: So as Rajesh said, my name is Chris. 00:01:28.840 --> 00:01:31.460 I'm an attorney in DC. 00:01:31.460 --> 00:01:34.220 I do mostly environmental work. 00:01:34.220 --> 00:01:37.097 I do some other pharmaceutical work, 00:01:37.097 --> 00:01:38.930 but my background's in chemical engineering. 00:01:38.930 --> 00:01:43.160 So I got a master's degree from MIT in chemical engineering, 00:01:43.160 --> 00:01:48.500 and I use that background to evaluate chemical exposures, 00:01:48.500 --> 00:01:51.830 to evaluate pharmaceutical exposures, to review-- 00:01:51.830 --> 00:01:54.920 and review of epidemiology, and toxicology, 00:01:54.920 --> 00:02:00.050 and related scientific subjects to the cases that I bring. 00:02:00.050 --> 00:02:04.520 And as part of that, I use the whole spectrum 00:02:04.520 --> 00:02:06.890 of scientific data. 00:02:06.890 --> 00:02:12.590 Some of that is very high tech, lab-based, mathematical 00:02:12.590 --> 00:02:17.060 modeling, obviously, you know, EPA type sampling. 00:02:17.060 --> 00:02:19.114 But then I also use-- 00:02:19.114 --> 00:02:20.780 at sort of the other end of the spectrum 00:02:20.780 --> 00:02:24.074 is more citizen science type data. 00:02:24.074 --> 00:02:26.240 We'll start by talking about a few examples starting 00:02:26.240 --> 00:02:29.840 with photography, but people don't think of photography 00:02:29.840 --> 00:02:30.680 as being data. 00:02:30.680 --> 00:02:34.320 But the same standards apply in court, 00:02:34.320 --> 00:02:38.120 whether it's a sample taken by an expert with multiple degrees 00:02:38.120 --> 00:02:41.900 to a picture taken by someone on the street corner. 00:02:41.900 --> 00:02:44.390 And so, I think photography is a good starting point 00:02:44.390 --> 00:02:47.330 to talk about how to get things admitted into court, 00:02:47.330 --> 00:02:53.630 and how you can use these types of information, data, 00:02:53.630 --> 00:02:57.620 to advocate for your cause-- to prove a case whether it's 00:02:57.620 --> 00:03:02.030 to oppose a permit, or to oppose an expansion of a facility, 00:03:02.030 --> 00:03:06.200 or to generate some finding of liability 00:03:06.200 --> 00:03:08.880 on behalf of your client. 00:03:08.880 --> 00:03:12.710 So we're going to see if I can do this with my phone, 00:03:12.710 --> 00:03:15.512 and it seems like it's working. 00:03:15.512 --> 00:03:16.970 The important thing, when you start 00:03:16.970 --> 00:03:20.530 talking about scientific evidence, 00:03:20.530 --> 00:03:22.250 I get all kinds of questions where 00:03:22.250 --> 00:03:24.080 someone will say, you know, we're 00:03:24.080 --> 00:03:25.760 looking to fight this issue. 00:03:25.760 --> 00:03:28.130 We want to go to court, and we want our data 00:03:28.130 --> 00:03:31.400 to be good in court. 00:03:31.400 --> 00:03:32.940 So we want data that's the best. 00:03:32.940 --> 00:03:34.670 We want it to be good in court. 00:03:34.670 --> 00:03:40.370 And the reality there's no golden rule 00:03:40.370 --> 00:03:43.460 for what's admissible in court. 00:03:43.460 --> 00:03:46.950 You can have-- I had a case in Massachusetts outside Boston, 00:03:46.950 --> 00:03:52.050 a PCB case where the EPA had done hundreds and thousands 00:03:52.050 --> 00:03:53.960 of sampling testing-- 00:03:53.960 --> 00:03:57.110 where they had sampled the ground for PCBs, 00:03:57.110 --> 00:03:59.600 and it was all EPA sampling. 00:03:59.600 --> 00:04:01.645 It was not part of litigation. 00:04:01.645 --> 00:04:04.160 It wasn't done by me or my experts, 00:04:04.160 --> 00:04:06.710 and despite the fact that it was EPA sampling 00:04:06.710 --> 00:04:09.410 done by EPA methods and protocols, 00:04:09.410 --> 00:04:11.270 the defendant still challenged the data. 00:04:11.270 --> 00:04:15.200 I mean, they say, well, it's not reliable because it was EPA. 00:04:15.200 --> 00:04:18.269 So there's no answer that says, well, 00:04:18.269 --> 00:04:22.940 if you do a, b, and c, and dot all the I's in there, 00:04:22.940 --> 00:04:24.440 that it will be admitted. 00:04:24.440 --> 00:04:27.230 And I think, to the other point is, 00:04:27.230 --> 00:04:30.020 if you haven't done a, b, and c, you still 00:04:30.020 --> 00:04:32.900 may get your data in front of the jury 00:04:32.900 --> 00:04:35.030 or in front of the judge, depending 00:04:35.030 --> 00:04:39.020 on the assessment of essentially these factors, which 00:04:39.020 --> 00:04:41.270 the first thing is whether the data is 00:04:41.270 --> 00:04:43.400 reliable and repeatable. 00:04:43.400 --> 00:04:46.670 So for example, I had a case, that I'll 00:04:46.670 --> 00:04:50.060 give you a couple examples from, that involved 00:04:50.060 --> 00:04:51.910 sampling for bacteria. 00:04:51.910 --> 00:04:54.480 It was a chicken house that had discharges 00:04:54.480 --> 00:04:55.820 going to the waters of the US. 00:04:55.820 --> 00:04:59.450 It was a Clean Water Act case, and there was a question about 00:04:59.450 --> 00:05:03.250 whether there was bacteria as well as some nutrients 00:05:03.250 --> 00:05:06.730 in the water-- so phosphorous and nitrogen-- 00:05:06.730 --> 00:05:08.590 that were polluting the waterways. 00:05:08.590 --> 00:05:13.270 And questions that become relevant are 00:05:13.270 --> 00:05:15.520 is a person wearing gloves, is there 00:05:15.520 --> 00:05:17.969 sterile procedures used, are the containers 00:05:17.969 --> 00:05:20.260 that they're going to take a sample of the water from-- 00:05:20.260 --> 00:05:21.850 are they sterile to begin with? 00:05:21.850 --> 00:05:24.790 Obviously, if there's any question about those things, 00:05:24.790 --> 00:05:28.570 it calls into question the reliability and repeatability 00:05:28.570 --> 00:05:29.660 of the data. 00:05:29.660 --> 00:05:32.320 So that's the first real prong. 00:05:32.320 --> 00:05:36.070 The second prong, and this is pretty loaded, 00:05:36.070 --> 00:05:38.720 is whether it's fit for the intended purpose. 00:05:38.720 --> 00:05:42.710 So if you're trying to prove, for example, 00:05:42.710 --> 00:05:46.630 the presence of benzene in somebody's drinking water, 00:05:46.630 --> 00:05:50.080 and you have a certain test that detects benzene, 00:05:50.080 --> 00:05:52.705 and maybe it gives it a number. 00:05:52.705 --> 00:05:54.580 If the question for the jury is whether there 00:05:54.580 --> 00:05:59.050 was benzene in the water, there may be a different standard 00:05:59.050 --> 00:06:01.120 than if the question is was there 00:06:01.120 --> 00:06:04.240 enough benzene in the water for the last 15 years 00:06:04.240 --> 00:06:06.970 to give my client leukemia? 00:06:06.970 --> 00:06:09.400 And so, there becomes this question 00:06:09.400 --> 00:06:11.500 about what is the purpose that you're 00:06:11.500 --> 00:06:15.130 offering the evidence for, which then 00:06:15.130 --> 00:06:18.820 relates this third point, which is given your purpose, 00:06:18.820 --> 00:06:21.100 is it going to assist the jury? 00:06:21.100 --> 00:06:25.180 So if that information, to whatever degree 00:06:25.180 --> 00:06:29.920 it falls in that scientific rigor spectrum, 00:06:29.920 --> 00:06:32.270 how is that going to help the jury understand? 00:06:32.270 --> 00:06:36.880 So maybe we have a test that shows the presence of benzene, 00:06:36.880 --> 00:06:38.360 but we don't trust the number. 00:06:38.360 --> 00:06:41.710 But we have an expert that does groundwater modeling that 00:06:41.710 --> 00:06:43.210 can say, well, we have a number that 00:06:43.210 --> 00:06:44.800 shows the presence of benzene. 00:06:44.800 --> 00:06:46.210 On top of that, we have an expert 00:06:46.210 --> 00:06:49.192 that modeled out the groundwater for the last 15 years 00:06:49.192 --> 00:06:51.400 and can show the fluctuations in those concentrations 00:06:51.400 --> 00:06:52.900 over the last 15 years-- 00:06:52.900 --> 00:06:55.900 the combination of which gives you 00:06:55.900 --> 00:06:58.690 the juror or the judge confidence that there 00:06:58.690 --> 00:07:00.400 was, in fact, benzene in the water, 00:07:00.400 --> 00:07:03.550 and it was within a certain range that, I might argue, 00:07:03.550 --> 00:07:06.820 caused a person to get sick. 00:07:06.820 --> 00:07:09.700 So with that being said, let's see. 00:07:09.700 --> 00:07:11.180 Oh, do I have-- 00:07:11.180 --> 00:07:13.640 is it going to be? 00:07:13.640 --> 00:07:16.180 Do I not know what I'm doing? 00:07:16.180 --> 00:07:16.930 OK. 00:07:16.930 --> 00:07:18.520 So here's a simple example. 00:07:18.520 --> 00:07:22.120 So I represent-- there's a lot of people in DC. 00:07:22.120 --> 00:07:25.360 There's probably a lot of people in a lot of different cities. 00:07:25.360 --> 00:07:27.070 It's not glamorous by any stretch, 00:07:27.070 --> 00:07:29.170 but there are a lot of people that 00:07:29.170 --> 00:07:31.570 live in subsidized housing in this country that 00:07:31.570 --> 00:07:35.210 is sub, sub, sub standard. 00:07:35.210 --> 00:07:37.090 So there are people that, whether it's 00:07:37.090 --> 00:07:39.670 the state or its HUD, the federal government, 00:07:39.670 --> 00:07:41.620 are paying for them. 00:07:41.620 --> 00:07:43.560 In a city like DC, they're paying, 00:07:43.560 --> 00:07:49.930 you know, $1,500 a month for a mother and her child 00:07:49.930 --> 00:07:55.030 to live in conditions that are unhealthy for both the mother 00:07:55.030 --> 00:07:58.330 and for the child, and that the government's paying 00:07:58.330 --> 00:08:00.700 the bill for, and somebody is profiting 00:08:00.700 --> 00:08:02.504 from at the expense of these people living 00:08:02.504 --> 00:08:04.420 in there and at the expense of the government. 00:08:04.420 --> 00:08:09.730 And so, this is an easy example, where someone takes a picture. 00:08:09.730 --> 00:08:11.740 I picked a picture of this as, you know, 00:08:11.740 --> 00:08:15.580 water coming through the ceiling of this apartment 00:08:15.580 --> 00:08:17.480 where you have mold. 00:08:17.480 --> 00:08:19.670 We are currently-- 00:08:19.670 --> 00:08:21.530 I don't know why we're losing our picture. 00:08:21.530 --> 00:08:23.620 Oh, there we go. 00:08:23.620 --> 00:08:27.830 So we represent-- in some cases, we represent the government 00:08:27.830 --> 00:08:30.310 on what's called the whistle blower lawsuit, where we're 00:08:30.310 --> 00:08:34.120 representing the government against these landlords 00:08:34.120 --> 00:08:36.280 for collecting money from the government, 00:08:36.280 --> 00:08:39.539 representing that these are habitable healthy places 00:08:39.539 --> 00:08:41.830 that they're getting paid a significant amount of money 00:08:41.830 --> 00:08:44.500 for-- for people to come, and get sick, and end up 00:08:44.500 --> 00:08:46.642 at the emergency room with asthma attacks 00:08:46.642 --> 00:08:48.850 when they can't breathe anymore, because they've been 00:08:48.850 --> 00:08:50.530 living in these conditions. 00:08:50.530 --> 00:08:53.150 I could have-- there was a horrible situation. 00:08:53.150 --> 00:08:57.190 And again, it's sort of a simplified approach to data, 00:08:57.190 --> 00:09:01.780 but in one of these apartments, you go in there, 00:09:01.780 --> 00:09:04.150 and there were so many bugs. 00:09:04.150 --> 00:09:05.700 I mean, you couldn't-- 00:09:05.700 --> 00:09:08.440 it was the craziest thing. 00:09:08.440 --> 00:09:10.870 But the picture is worth a thousand words, 00:09:10.870 --> 00:09:12.790 and there becomes a question, like there 00:09:12.790 --> 00:09:14.915 would be with any data, as to who took the picture? 00:09:14.915 --> 00:09:15.700 When was it? 00:09:15.700 --> 00:09:18.100 Can they certify that it was in the apartment? 00:09:18.100 --> 00:09:18.910 That's at issue. 00:09:18.910 --> 00:09:21.670 And at the time-- and, you know, that they didn't-- 00:09:21.670 --> 00:09:24.250 it wasn't a bunch of plastic cockroaches that they put, 00:09:24.250 --> 00:09:27.580 but it was in fact a bunch of bugs crawling around. 00:09:27.580 --> 00:09:30.270 Oh, and this, the farmer trenched the ditch. 00:09:30.270 --> 00:09:33.430 So the purpose here would be to show visible evidence of mold 00:09:33.430 --> 00:09:35.960 as opposed to the farmer trenching 00:09:35.960 --> 00:09:38.620 the ditch, which we'll get to. 00:09:38.620 --> 00:09:41.020 So you're going to have a witness that comes in and says, 00:09:41.020 --> 00:09:42.103 yeah, I took that picture. 00:09:42.103 --> 00:09:44.230 I took it three months ago. 00:09:44.230 --> 00:09:47.890 I remember because it was two weeks 00:09:47.890 --> 00:09:49.990 after the water was dripping from the ceiling, 00:09:49.990 --> 00:09:52.300 and here's the mold. 00:09:52.300 --> 00:09:55.360 Again, if we were trying to prove a housing code violation, 00:09:55.360 --> 00:09:56.927 the picture is probably sufficient. 00:09:56.927 --> 00:09:59.260 If we're trying to prove that somebody has asthma attack 00:09:59.260 --> 00:10:02.310 two weeks later was caused by this mold, that's 00:10:02.310 --> 00:10:04.560 where you get into some more sophisticated sampling 00:10:04.560 --> 00:10:06.390 and having an industrial hygienist 00:10:06.390 --> 00:10:09.010 come in and do some testing. 00:10:09.010 --> 00:10:13.950 So this is another type of litigation 00:10:13.950 --> 00:10:16.290 that I've been involved in. 00:10:16.290 --> 00:10:19.230 For those people that aren't aware, 00:10:19.230 --> 00:10:26.677 a good bit of our sewage sludge, human waste, that we generate 00:10:26.677 --> 00:10:28.260 goes from the sewage treatment plants, 00:10:28.260 --> 00:10:33.120 gets collected as solids, and then gets sent to farms. 00:10:33.120 --> 00:10:35.850 In this case, it was a quote unquote, tree farm, 00:10:35.850 --> 00:10:39.060 but it's sent to farms that grow crops. 00:10:39.060 --> 00:10:42.810 And they put human waste on crops-- 00:10:42.810 --> 00:10:45.510 the argument being that it's treated. 00:10:45.510 --> 00:10:47.030 The treating is relatively minimal, 00:10:47.030 --> 00:10:53.640 and they essentially put lime in with the fecal material. 00:10:53.640 --> 00:10:56.100 And they then use it as fertilizer. 00:10:56.100 --> 00:11:00.420 So as you can imagine it's full of pharmaceuticals-- 00:11:00.420 --> 00:11:01.860 it's full of industrial chemicals, 00:11:01.860 --> 00:11:05.602 because the local industries all contribute to the sewer system. 00:11:05.602 --> 00:11:07.310 There's hospital waste in there, so there 00:11:07.310 --> 00:11:09.360 is viruses, pathogens, all these things 00:11:09.360 --> 00:11:13.700 that you would expect in human feces-- 00:11:13.700 --> 00:11:14.560 exist in there. 00:11:14.560 --> 00:11:19.170 And in this case, this was a 1,300-acre forest that they 00:11:19.170 --> 00:11:22.500 spread this stuff in, and my client lived in the middle 00:11:22.500 --> 00:11:24.870 of the forest. 00:11:24.870 --> 00:11:28.260 She ended up with pulmonary fibrosis with her lungs 00:11:28.260 --> 00:11:31.680 that were getting scarred from repeated 00:11:31.680 --> 00:11:35.610 inflammation from breathing in these proteins and stuff. 00:11:35.610 --> 00:11:38.640 And so, this was one of her pictures of what was going on. 00:11:38.640 --> 00:11:40.740 Again, like, not what we necessarily 00:11:40.740 --> 00:11:45.420 think of with respect to data, but it 00:11:45.420 --> 00:11:48.030 creates a compelling picture, and you still 00:11:48.030 --> 00:11:50.790 have that same standard of getting 00:11:50.790 --> 00:11:53.580 it admitted for the court. 00:11:53.580 --> 00:11:56.320 This is a picture from the case that I referenced a little bit 00:11:56.320 --> 00:11:56.820 ago. 00:11:56.820 --> 00:12:02.670 This is a poultry operation that we have there 00:12:02.670 --> 00:12:05.790 that we had access on public property. 00:12:05.790 --> 00:12:09.000 The water keeper, or the local river keeper, 00:12:09.000 --> 00:12:11.970 had access on public property to this drainage ditch, 00:12:11.970 --> 00:12:15.030 and this is where we get into sort of a combined 00:12:15.030 --> 00:12:19.534 use of different media or data. 00:12:19.534 --> 00:12:21.450 The water keeper is able to come to this ditch 00:12:21.450 --> 00:12:26.490 on public property and take water samples, which she did, 00:12:26.490 --> 00:12:28.030 I think, 12 or 14 times. 00:12:28.030 --> 00:12:31.080 So she took water samples, which showed 00:12:31.080 --> 00:12:32.910 high levels of bacteria-- 00:12:32.910 --> 00:12:35.460 fecal coliform, and E. coli, as well as 00:12:35.460 --> 00:12:38.336 high levels of nutrients going through the ditch, 00:12:38.336 --> 00:12:39.960 across the highway, and then eventually 00:12:39.960 --> 00:12:43.860 out into the waters of the US. 00:12:43.860 --> 00:12:45.870 And what this photo actually is is this 00:12:45.870 --> 00:12:48.150 is a photo from Google Earth. 00:12:48.150 --> 00:12:51.760 It's just a Google Earth satellite photo. 00:12:51.760 --> 00:12:55.170 Another point of issue was, at the top corner there, 00:12:55.170 --> 00:12:57.630 there was a pile of some unknown material. 00:12:57.630 --> 00:13:00.240 And we'll see a close up in a later picture 00:13:00.240 --> 00:13:04.090 that sort of shows some of what was going on on the farm. 00:13:04.090 --> 00:13:06.810 And then you have the pictures of the chicken houses. 00:13:06.810 --> 00:13:10.350 And then you have what's also a cattle operation in the bottom 00:13:10.350 --> 00:13:14.950 right there that I'm going to show you another picture of it. 00:13:14.950 --> 00:13:18.720 So again, this is Google Earth. 00:13:18.720 --> 00:13:20.850 We went to trial in this case. 00:13:20.850 --> 00:13:25.290 We got these pictures admitted from Google Earth. 00:13:25.290 --> 00:13:26.850 And one of the things that we saw-- 00:13:26.850 --> 00:13:31.050 and I was deposing the defendants-- 00:13:31.050 --> 00:13:35.430 is that the property had poultry operation that was run by-- 00:13:35.430 --> 00:13:38.100 well, that was owned by Purdue-- 00:13:38.100 --> 00:13:42.660 run by their quote, unquote, family farmer. 00:13:42.660 --> 00:13:44.640 And we believe that that was discharging 00:13:44.640 --> 00:13:48.330 into that drainage ditch and out to where we sampled. 00:13:48.330 --> 00:13:53.070 There was also the cattle operation, which had probably 00:13:53.070 --> 00:13:55.590 some impact on the water. 00:13:55.590 --> 00:13:57.175 But what was interesting was-- 00:13:57.175 --> 00:13:58.800 as part of the Clean Water Act lawsuit, 00:13:58.800 --> 00:14:02.140 we had to notify them that we were planning to sue them. 00:14:02.140 --> 00:14:06.510 And at some point, what you see on the aerial photo-- 00:14:06.510 --> 00:14:08.700 as you can see, you know, tiny there, 00:14:08.700 --> 00:14:13.110 but basically the cattle were then moved from the cattle 00:14:13.110 --> 00:14:15.300 grazing area down in the far right-- 00:14:15.300 --> 00:14:17.250 if we the looked on the previous photo, 00:14:17.250 --> 00:14:20.670 where you see all the cattle markings from them in the two 00:14:20.670 --> 00:14:22.470 lower right fields-- 00:14:22.470 --> 00:14:26.080 to a feeder that's up in the top there. 00:14:26.080 --> 00:14:28.650 So you can see the cows parked around some hay 00:14:28.650 --> 00:14:31.520 bales or a feeder with a bunch of hay on there 00:14:31.520 --> 00:14:35.760 to create the impression that the cows weren't just 00:14:35.760 --> 00:14:36.810 over to the far right. 00:14:36.810 --> 00:14:39.739 They were more by this ditch even though-- 00:14:39.739 --> 00:14:41.280 and that's just something we randomly 00:14:41.280 --> 00:14:43.770 picked up on Google Earth. 00:14:43.770 --> 00:14:45.270 So when I'm in deposition with them, 00:14:45.270 --> 00:14:47.220 and I ask them, well, where do the cows hang out-- 00:14:47.220 --> 00:14:49.511 and they tell me, oh, they hang out all over the place. 00:14:49.511 --> 00:14:52.530 And you can see, and there was a whole series 00:14:52.530 --> 00:14:55.020 of photos, as you can imagine, from Google Earth that 00:14:55.020 --> 00:15:00.180 show that, consistent with the markings in these areas, 00:15:00.180 --> 00:15:02.620 that the cows and their feeders mostly 00:15:02.620 --> 00:15:08.000 are in here, some in here, and pretty rarely up in there. 00:15:08.000 --> 00:15:11.220 And so, this is the aerial photograph. 00:15:11.220 --> 00:15:14.860 So this is the pile that was at the top of that drainage ditch. 00:15:14.860 --> 00:15:17.290 This is the ditch that was in question. 00:15:17.290 --> 00:15:19.900 And so, we had the Google Earth photos, 00:15:19.900 --> 00:15:21.850 and then we also had aerial photos 00:15:21.850 --> 00:15:26.560 that the water keeper was able to get a flight 00:15:26.560 --> 00:15:29.260 and take some of their own photos. 00:15:29.260 --> 00:15:31.900 And here you had a pile which was 00:15:31.900 --> 00:15:34.210 sort of unknown, what it was, but it was pretty 00:15:34.210 --> 00:15:37.690 clear from these photos that the farmer had dug trenches 00:15:37.690 --> 00:15:40.540 to drain what was in those piles-- 00:15:40.540 --> 00:15:43.210 the runoff, the water that was accumulating there, 00:15:43.210 --> 00:15:44.620 into the farm runoff-- 00:15:44.620 --> 00:15:47.560 into the ditches that then, ultimately, 00:15:47.560 --> 00:15:49.360 went out to where we sampled. 00:15:49.360 --> 00:15:54.190 Then it ultimately went out to the waters of the US. 00:15:54.190 --> 00:15:57.790 So again, what's the purpose of this photo? 00:15:57.790 --> 00:15:59.750 What's the purpose of the previous photo? 00:15:59.750 --> 00:16:02.710 If the purpose is to show where the cows are, if the purpose is 00:16:02.710 --> 00:16:04.930 to show where they are in a given date 00:16:04.930 --> 00:16:07.950 given the image date from Google Earth, 00:16:07.950 --> 00:16:12.670 if the purpose here is to show that the farmer had indeed-- 00:16:12.670 --> 00:16:15.460 that there was drainage coming from this pile, which 00:16:15.460 --> 00:16:17.490 turned out to be-- 00:16:17.490 --> 00:16:19.420 it was actually sewage sludge. 00:16:19.420 --> 00:16:22.870 Unrelated case, but the farmer was also 00:16:22.870 --> 00:16:24.490 stockpiling sewage sludge. 00:16:24.490 --> 00:16:29.590 So it's material that's rich in nutrients as well as probably 00:16:29.590 --> 00:16:31.630 bacteria and pathogens. 00:16:31.630 --> 00:16:33.890 And he had trenched this, so is their drainage? 00:16:33.890 --> 00:16:35.140 What's the material? 00:16:35.140 --> 00:16:38.590 Did he intend to drain this material into the ditch? 00:16:38.590 --> 00:16:40.300 Those questions, I think, it's certainly 00:16:40.300 --> 00:16:43.140 is relevant, and fit for that purpose, 00:16:43.140 --> 00:16:45.580 and would be helpful to the jury. 00:16:45.580 --> 00:16:49.660 So this is the sampling point, and this became an issue 00:16:49.660 --> 00:16:50.660 for a couple of reasons. 00:16:50.660 --> 00:16:53.130 One is whether she was trespassing. 00:16:53.130 --> 00:16:55.600 Two is how she took the samples, as far 00:16:55.600 --> 00:16:57.430 as her sterile technique. 00:16:57.430 --> 00:17:00.580 Getting to the lab data that we use, we had, 00:17:00.580 --> 00:17:02.770 I think, it was 14 or 12 samples. 00:17:02.770 --> 00:17:05.099 And she went through, she wore gloves, 00:17:05.099 --> 00:17:07.480 she used sealed containers from the lab. 00:17:07.480 --> 00:17:09.349 She kept them cold. 00:17:09.349 --> 00:17:14.328 She had a chain of custody from the sampling point to the lab. 00:17:14.328 --> 00:17:16.369 And so, all of the questions where you say, well, 00:17:16.369 --> 00:17:17.420 do we need to hire a consultant? 00:17:17.420 --> 00:17:19.128 Do we need to pay somebody a lot of money 00:17:19.128 --> 00:17:23.170 to come in from another state and take a sample, 00:17:23.170 --> 00:17:25.119 or is it sufficient to have somebody that's 00:17:25.119 --> 00:17:30.340 got sort of the requisite training that would otherwise 00:17:30.340 --> 00:17:31.424 disqualify the sample? 00:17:31.424 --> 00:17:33.340 So if she didn't know about sterile technique, 00:17:33.340 --> 00:17:35.890 or if she hadn't worn sterile gloves, 00:17:35.890 --> 00:17:39.195 or protected the integrity of the sampling containers, 00:17:39.195 --> 00:17:41.320 those would be things that would call into question 00:17:41.320 --> 00:17:43.270 the reliability of the results. 00:17:43.270 --> 00:17:46.720 But the court ended up admitting the sample results-- 00:17:46.720 --> 00:17:48.250 didn't question the results-- 00:17:48.250 --> 00:17:50.860 because she was able to answer questions on her cross-exam 00:17:50.860 --> 00:17:53.080 that she had done all of the things 00:17:53.080 --> 00:17:56.920 that would otherwise disqualify those results. 00:17:56.920 --> 00:18:00.190 And so, there was some question about whether there was flow 00:18:00.190 --> 00:18:03.130 that the defendant said that there wasn't ever flow-- 00:18:03.130 --> 00:18:06.765 that there was barely ever water here. 00:18:06.765 --> 00:18:08.140 And so, these pictures, obviously 00:18:08.140 --> 00:18:10.960 came into play for that. 00:18:10.960 --> 00:18:13.810 This is at that public access point, 00:18:13.810 --> 00:18:17.750 and obviously that was helpful for that purpose. 00:18:17.750 --> 00:18:19.540 I just put this picture in here. 00:18:19.540 --> 00:18:20.380 I think Nathan-- 00:18:20.380 --> 00:18:22.740 I don't know if Nathan has an interest. 00:18:22.740 --> 00:18:25.320 Some of the work that I do on fracking-- 00:18:25.320 --> 00:18:27.850 and I do a lot of work with the folks down in Texas 00:18:27.850 --> 00:18:29.920 and the people in Pennsylvania. 00:18:29.920 --> 00:18:32.920 And so, it's difficult to see in this example. 00:18:32.920 --> 00:18:38.980 I just quickly pulled it off YouTube, but with FLIR camera, 00:18:38.980 --> 00:18:42.730 it's not unlike, in some sense, a standard photograph, which 00:18:42.730 --> 00:18:45.550 we've seen some examples of, but it's 00:18:45.550 --> 00:18:49.160 an $80,000 piece of equipment. 00:18:49.160 --> 00:18:53.350 But with such a camera, you can see emissions 00:18:53.350 --> 00:18:56.260 from this equipment that you can't see with the naked eye. 00:18:56.260 --> 00:18:58.030 And so, there's a piece of equipment here, 00:18:58.030 --> 00:19:00.310 and you can see all this billowing out 00:19:00.310 --> 00:19:03.250 of there, which is primarily volatile organic chemicals-- 00:19:03.250 --> 00:19:05.100 things like benzene, and toluene, 00:19:05.100 --> 00:19:08.710 and you name it from these storage tanks. 00:19:08.710 --> 00:19:10.684 And so, when you look at-- whether you're 00:19:10.684 --> 00:19:12.100 looking at climate change impacts, 00:19:12.100 --> 00:19:13.690 or whether you're looking at health 00:19:13.690 --> 00:19:19.540 and environmental impacts, or all of the above, you know, 00:19:19.540 --> 00:19:20.827 you have these tanks. 00:19:20.827 --> 00:19:21.910 You walk up close to them. 00:19:21.910 --> 00:19:23.470 You can smell them. 00:19:23.470 --> 00:19:25.780 But they are vented to the atmosphere. 00:19:25.780 --> 00:19:28.240 They're not being regulated. 00:19:28.240 --> 00:19:29.692 And you have people-- 00:19:29.692 --> 00:19:31.150 typically with respect to fracking, 00:19:31.150 --> 00:19:35.830 you have people that are living in conjunction with them 00:19:35.830 --> 00:19:37.420 in close proximity. 00:19:37.420 --> 00:19:41.170 You might have a swimming pool or a playground 00:19:41.170 --> 00:19:43.810 in somebody's side yard that's within a few hundred 00:19:43.810 --> 00:19:45.920 feet of some of these tanks. 00:19:45.920 --> 00:19:49.580 And so, again, what's the purpose? 00:19:49.580 --> 00:19:52.510 Is the purpose to show that, in fact, there are emissions? 00:19:52.510 --> 00:19:56.230 Is the purpose to show that the four-year-old is 00:19:56.230 --> 00:20:00.570 having breathing problems as a result of these emissions? 00:20:00.570 --> 00:20:04.320 From my perspective, if the purpose is to show emissions, 00:20:04.320 --> 00:20:06.410 you have someone testify. 00:20:06.410 --> 00:20:08.490 Five minutes? 00:20:08.490 --> 00:20:13.560 That's perfect-- you know, what the FLIR camera does, 00:20:13.560 --> 00:20:15.540 how it does it, and what it shows-- 00:20:15.540 --> 00:20:18.189 which I think would validate that there are admissions. 00:20:18.189 --> 00:20:20.730 If the purpose was to show that somebody was having breathing 00:20:20.730 --> 00:20:24.480 problems or was getting cancer from that, you would probably 00:20:24.480 --> 00:20:27.810 also have, for example, some other grab samples, some air 00:20:27.810 --> 00:20:31.180 testing, that actually qualified or quantified 00:20:31.180 --> 00:20:34.200 the amounts of benzene, and toluene, and other things that 00:20:34.200 --> 00:20:37.410 are in the air that would then be corroborated 00:20:37.410 --> 00:20:40.360 with this visual evidence that says, in fact, 00:20:40.360 --> 00:20:42.220 there is something coming from the tank. 00:20:42.220 --> 00:20:44.220 And we're picking it up in the front yard 00:20:44.220 --> 00:20:47.550 of my client or the person that's complaining. 00:20:47.550 --> 00:20:49.710 So I think that this is an example. 00:20:49.710 --> 00:20:52.454 Unfortunately, it's out of the hands-- 00:20:52.454 --> 00:20:54.870 financially out of the hands of most people at this point, 00:20:54.870 --> 00:20:57.450 but it's a really powerful tool with respect 00:20:57.450 --> 00:21:00.180 to not just health but also climate change 00:21:00.180 --> 00:21:04.200 issues with methane leaks and other things. 00:21:04.200 --> 00:21:07.930 This is an example outside Pittsburgh 00:21:07.930 --> 00:21:09.750 that I'm looking at right now. 00:21:09.750 --> 00:21:12.150 This is from modeling that was done-- 00:21:12.150 --> 00:21:14.910 and this is cancer risk based on Coke emissions. 00:21:14.910 --> 00:21:18.420 And so, I'm looking at a case around a Coke plant 00:21:18.420 --> 00:21:20.580 outside Pittsburgh, where you have 00:21:20.580 --> 00:21:24.660 people that are living in the shadows of this huge Coke 00:21:24.660 --> 00:21:27.240 plant, one of the few remaining in Pittsburgh. 00:21:27.240 --> 00:21:30.180 And this is sort of the rigorous data that's 00:21:30.180 --> 00:21:32.700 looked at based on air emission data and modeling 00:21:32.700 --> 00:21:36.780 of those emissions, where you can see these huge cancer risks 00:21:36.780 --> 00:21:40.030 as a result of the emissions. 00:21:40.030 --> 00:21:42.120 This is another thing that's going 00:21:42.120 --> 00:21:46.820 on in that same community, which is this is-- 00:21:46.820 --> 00:21:47.960 I think this is called-- 00:21:47.960 --> 00:21:50.040 I forget the name of this device, 00:21:50.040 --> 00:21:54.960 but it's one of these internet of things connected devices 00:21:54.960 --> 00:21:56.760 that looks at air quality. 00:21:56.760 --> 00:21:59.610 So is it is it lab-certified data? 00:21:59.610 --> 00:22:01.950 I mean, this example it said 200 high. 00:22:01.950 --> 00:22:04.200 The air quality is bad. 00:22:04.200 --> 00:22:07.710 Again, am I trying to show that half the neighborhood has 00:22:07.710 --> 00:22:09.420 asthma because of this, or am I trying 00:22:09.420 --> 00:22:12.030 to show that they have decreased the property values 00:22:12.030 --> 00:22:13.860 because their air quality is bad, 00:22:13.860 --> 00:22:18.600 or am I just trying to show that the Coke ovens are impacting 00:22:18.600 --> 00:22:20.100 the air quality locally? 00:22:20.100 --> 00:22:22.260 And then I have the other data, the previous data, 00:22:22.260 --> 00:22:25.260 where I have modeling to show the real nature 00:22:25.260 --> 00:22:29.070 and extent of risk and injury. 00:22:29.070 --> 00:22:31.240 This is another similar device. 00:22:31.240 --> 00:22:33.330 These are guys that I've talked to about trying 00:22:33.330 --> 00:22:35.520 to use in some of my cases, where 00:22:35.520 --> 00:22:38.580 you could take for a couple hundred, $200 or $300. 00:22:38.580 --> 00:22:43.440 This one's called a uHoo, and you could distribute these 00:22:43.440 --> 00:22:47.130 throughout a neighborhood and show that, for example, 00:22:47.130 --> 00:22:48.330 the pattern of emissions. 00:22:48.330 --> 00:22:51.331 You could show that the emissions, or the air 00:22:51.331 --> 00:22:53.580 pollutants, are at the highest level near the facility 00:22:53.580 --> 00:22:57.120 and decay as you get farther out from the facility. 00:22:57.120 --> 00:22:59.470 What you might also try to show are things 00:22:59.470 --> 00:23:00.720 like the pattern of emissions. 00:23:00.720 --> 00:23:02.850 So a lot of times, industries will-- 00:23:05.970 --> 00:23:07.710 you know, smart on their part-- 00:23:07.710 --> 00:23:10.050 will wait until between 2:00 and 5:00 00:23:10.050 --> 00:23:13.500 in the morning to let things fly. 00:23:13.500 --> 00:23:15.570 And so, I have a case in Michigan 00:23:15.570 --> 00:23:20.670 where the refinery there is alleged, 00:23:20.670 --> 00:23:22.380 by the people living near it-- 00:23:22.380 --> 00:23:24.106 between 2:00 and 5:00 in the morning, 00:23:24.106 --> 00:23:25.230 they smell the rotten eggs. 00:23:25.230 --> 00:23:27.720 They smell the chemicals, and they wake up 00:23:27.720 --> 00:23:29.310 with difficulty breathing. 00:23:29.310 --> 00:23:35.130 And so, again, you put something like this inside your house 00:23:35.130 --> 00:23:36.660 or just outside your house. 00:23:36.660 --> 00:23:38.160 It's capturing data. 00:23:38.160 --> 00:23:42.570 And is the data being used to prove that you got lymphoma 00:23:42.570 --> 00:23:44.850 or leukemia from that, or is it being 00:23:44.850 --> 00:23:49.470 used to show that every night, at between 2:00 and 4:00, 00:23:49.470 --> 00:23:50.710 you get a spike? 00:23:50.710 --> 00:23:54.690 Whatever that number is, it may not be scientifically credible 00:23:54.690 --> 00:23:59.370 to the 0.00, but does it show that there is some pattern 00:23:59.370 --> 00:24:00.240 that-- 00:24:00.240 --> 00:24:02.400 or when the weather changes, that you get a shift, 00:24:02.400 --> 00:24:04.800 and that weather is consistent with winds 00:24:04.800 --> 00:24:09.010 blowing from the facility to your point of testing. 00:24:09.010 --> 00:24:11.610 So again, it really gets back to this question 00:24:11.610 --> 00:24:15.150 about what is it that you're intending to use the data for? 00:24:15.150 --> 00:24:18.240 This is-- what do I have, like, two minutes? 00:24:18.240 --> 00:24:21.570 This is another example, when I gave a talk with Public Lab, 00:24:21.570 --> 00:24:23.880 that someone was doing in New York City, which 00:24:23.880 --> 00:24:25.920 I thought was really great-- 00:24:25.920 --> 00:24:27.660 was that they were using-- 00:24:27.660 --> 00:24:29.640 they were giving low-income people 00:24:29.640 --> 00:24:33.632 that were in subsidized housing these connected thermometers. 00:24:33.632 --> 00:24:35.590 And they were collecting data from their homes, 00:24:35.590 --> 00:24:38.070 so that in the winter they could see where 00:24:38.070 --> 00:24:39.780 people were living in sub-- 00:24:39.780 --> 00:24:42.140 you know, in freezing-- or maybe not freezing 00:24:42.140 --> 00:24:45.840 but uninhabitable conditions in their homes that were not 00:24:45.840 --> 00:24:47.280 being properly-- so they're living 00:24:47.280 --> 00:24:49.320 in public housing or subsidized housing, 00:24:49.320 --> 00:24:51.870 and they're not being treated properly. 00:24:51.870 --> 00:24:54.030 And so, it's really a simple-- you know, 00:24:54.030 --> 00:24:57.960 it's just a temperature test with a GPS tag that could show 00:24:57.960 --> 00:24:59.820 these homes-- 00:24:59.820 --> 00:25:02.560 and I guess a time tag as well, so time and temperature 00:25:02.560 --> 00:25:04.860 are essentially that we could do, 00:25:04.860 --> 00:25:07.200 for the most part, with our smartphones these days-- 00:25:07.200 --> 00:25:10.200 that set up legal arguments for these people 00:25:10.200 --> 00:25:12.660 to say that they're not being treated properly 00:25:12.660 --> 00:25:16.140 and that they need adequate housing. 00:25:16.140 --> 00:25:20.640 So to sort of bring this a little bit to a head, 00:25:20.640 --> 00:25:26.814 better doesn't always mean more, and sometimes more data, 00:25:26.814 --> 00:25:28.230 even if it's qualitative data, can 00:25:28.230 --> 00:25:31.420 be better to prove a certain point, 00:25:31.420 --> 00:25:33.962 depending on what that point is. 00:25:33.962 --> 00:25:35.670 And so, there's this balance, or tension, 00:25:35.670 --> 00:25:39.000 between quality and quantity of data that largely relates back 00:25:39.000 --> 00:25:40.920 to money. 00:25:40.920 --> 00:25:43.380 So sort of to give you an example, 00:25:43.380 --> 00:25:45.880 this is that Coke facility in the top right. 00:25:45.880 --> 00:25:47.530 You can see part of it. 00:25:47.530 --> 00:25:50.670 And if we were to go and take samples 00:25:50.670 --> 00:25:53.790 throughout the neighborhood, they all cost money. 00:25:53.790 --> 00:25:55.500 If we were to take them with a PhD 00:25:55.500 --> 00:25:57.120 from the University of Pittsburgh, 00:25:57.120 --> 00:25:59.095 it would cost even more money. 00:25:59.095 --> 00:26:00.720 And so, the question becomes what is it 00:26:00.720 --> 00:26:03.000 that we are trying to prove? 00:26:03.000 --> 00:26:06.690 And if we're trying to prove that the levels of air 00:26:06.690 --> 00:26:09.330 pollutants are highest near the facility, 00:26:09.330 --> 00:26:11.430 and they decay as you get farther away, 00:26:11.430 --> 00:26:15.919 and that they spike at night, that that may be-- 00:26:15.919 --> 00:26:17.460 a certain level of data quality might 00:26:17.460 --> 00:26:19.350 be suitable for that versus trying 00:26:19.350 --> 00:26:22.050 to prove that someone's lung cancer or some other health 00:26:22.050 --> 00:26:27.100 issues were a result of that exposure. 00:26:27.100 --> 00:26:31.800 And I guess the converse is it may, in times-- 00:26:31.800 --> 00:26:33.750 and I think this is a good example-- 00:26:33.750 --> 00:26:39.360 be better to have more data of an arguable lesser quality 00:26:39.360 --> 00:26:44.430 than to have rigorous sampling done on three houses 00:26:44.430 --> 00:26:46.590 but not to have that full spectrum that 00:26:46.590 --> 00:26:49.980 shows what's happening as you get farther and farther away, 00:26:49.980 --> 00:26:52.680 because you focused your time, effort, and ultimately 00:26:52.680 --> 00:26:55.870 your money on sampling those things that 00:26:55.870 --> 00:26:59.500 were located where you had one sick person and that's it. 00:26:59.500 --> 00:27:00.750 Then you get into a question-- 00:27:00.750 --> 00:27:03.840 OK, I've got a great number outside this person's house, 00:27:03.840 --> 00:27:06.180 but I'm not sure how it relates back to the facility, 00:27:06.180 --> 00:27:08.513 and I'm not sure how the facility is impacting everybody 00:27:08.513 --> 00:27:10.300 else in the neighborhood. 00:27:10.300 --> 00:27:15.750 And I think that is-- so I think this is my last slide. 00:27:15.750 --> 00:27:18.900 Obviously, more money you pay for people 00:27:18.900 --> 00:27:21.990 with degrees, and consultants, and for lab testing-- 00:27:21.990 --> 00:27:26.520 you can save money with some of these techniques, citizen 00:27:26.520 --> 00:27:29.730 science, some of these taking your own samples 00:27:29.730 --> 00:27:32.310 with the appropriate training and technique-- 00:27:32.310 --> 00:27:34.050 using some of these connected devices, 00:27:34.050 --> 00:27:36.875 using some photography and FLIR cameras. 00:27:40.180 --> 00:27:43.740 More money doesn't always mean better, 00:27:43.740 --> 00:27:45.660 but it tends to mean that you've got 00:27:45.660 --> 00:27:48.690 better numbers, more precise numbers, that 00:27:48.690 --> 00:27:49.950 are more reliable. 00:27:49.950 --> 00:27:53.190 But again, the question is what is your purpose? 00:27:53.190 --> 00:27:55.606 If your purpose is to show you know 00:27:55.606 --> 00:27:57.480 that the source of the pollution is impacting 00:27:57.480 --> 00:27:59.550 the neighborhood in a broad sense, 00:27:59.550 --> 00:28:04.950 you can probably get by with some data of lesser rigor 00:28:04.950 --> 00:28:06.360 and that's lesser expense. 00:28:06.360 --> 00:28:08.120 If you're trying to prove someone's 00:28:08.120 --> 00:28:11.250 been exposed for how long at what level 00:28:11.250 --> 00:28:13.540 precisely to show that they got sick, 00:28:13.540 --> 00:28:15.810 obviously the standards are increased, 00:28:15.810 --> 00:28:20.530 and the level of scientific rigor is heightened as well. 00:28:20.530 --> 00:28:24.900 So I think that's it for me, and I'll pass-- 00:28:24.900 --> 00:28:27.160 I'll answer any questions. 00:28:27.160 --> 00:28:28.125 I'll receive applause. 00:28:32.900 --> 00:28:36.610 The question was to what extent trespassing 00:28:36.610 --> 00:28:39.100 on people's land and drones-- 00:28:39.100 --> 00:28:40.630 it's a good question. 00:28:40.630 --> 00:28:43.900 Drones are, you know, for some people, maybe 00:28:43.900 --> 00:28:48.045 not so new, but as far as people like me, a newer thing. 00:28:48.045 --> 00:28:49.420 But, I mean, drones are a great-- 00:28:49.420 --> 00:28:50.470 I mean, huge. 00:28:50.470 --> 00:28:54.020 Like, the aerial photos that were taken with a full flight-- 00:28:54.020 --> 00:28:56.020 like, having to rent a plane and all that stuff, 00:28:56.020 --> 00:28:58.300 it could be done with drones very easily-- 00:28:58.300 --> 00:29:00.250 get even better quality pictures. 00:29:00.250 --> 00:29:04.360 Drones are a huge, huge benefit to the citizen science 00:29:04.360 --> 00:29:05.219 operation. 00:29:05.219 --> 00:29:07.510 I mean, they have drones that can also take air samples 00:29:07.510 --> 00:29:11.980 and things like that, so the ability to get data 00:29:11.980 --> 00:29:14.740 cheaply, and efficiently, and effectively is a big thing. 00:29:14.740 --> 00:29:19.090 As far as trespassing goes, in my opinion, 00:29:19.090 --> 00:29:20.522 you never want to trespass. 00:29:20.522 --> 00:29:21.980 You never want to represent someone 00:29:21.980 --> 00:29:24.370 who's going to have somebody else saying, well, 00:29:24.370 --> 00:29:25.690 you trespassed. 00:29:25.690 --> 00:29:29.092 By the same token, I have had cases 00:29:29.092 --> 00:29:31.300 where people trespass on the land, and took a sample, 00:29:31.300 --> 00:29:31.990 and went home. 00:29:31.990 --> 00:29:34.124 And as a lawyer, I have an obligation 00:29:34.124 --> 00:29:36.040 to disclose the fact that they took a sample-- 00:29:36.040 --> 00:29:37.930 the result of that sampling. 00:29:37.930 --> 00:29:43.870 And aside from any allegations or claims of criminal trespass, 00:29:43.870 --> 00:29:45.790 if the data is reliable-- if they 00:29:45.790 --> 00:29:51.220 can't impugn what the person did-- that they somehow 00:29:51.220 --> 00:29:55.030 adulterated the sample, then the sample 00:29:55.030 --> 00:29:57.250 should be admissible for the purpose of showing 00:29:57.250 --> 00:29:58.750 what the sample shows, right? 00:29:58.750 --> 00:30:01.840 So there could be a dispute about whether there's 00:30:01.840 --> 00:30:04.090 some criminal trespass, which I think largely there's 00:30:04.090 --> 00:30:08.320 no impact from, but in general, the answer is if you can do it 00:30:08.320 --> 00:30:10.600 with a drone, and you can take pictures, 00:30:10.600 --> 00:30:12.250 if you can get a court order to get 00:30:12.250 --> 00:30:14.770 a sample on the property, that's a much better way 00:30:14.770 --> 00:30:16.730 to do it than trespassing. 00:30:16.730 --> 00:30:18.730 A lot of people don't understand the first thing 00:30:18.730 --> 00:30:21.500 you have to do is make a good case to the judge. 00:30:21.500 --> 00:30:25.270 So judges stand between you and the jury 00:30:25.270 --> 00:30:28.120 as far as scientific evidence-- as far as any evidence, right. 00:30:28.120 --> 00:30:31.840 And so, you first have to make a good argument to the judge 00:30:31.840 --> 00:30:35.140 that the data is reliable, fit for its intended purpose, 00:30:35.140 --> 00:30:36.790 and that it will assist the jury. 00:30:36.790 --> 00:30:38.650 What a jury will believe is oftentimes 00:30:38.650 --> 00:30:41.380 different than what a judge will believe, 00:30:41.380 --> 00:30:45.830 so something like a FLIR camera that shows visually 00:30:45.830 --> 00:30:48.440 the stream of pollution coming off 00:30:48.440 --> 00:30:50.590 may be very compelling to a jury even 00:30:50.590 --> 00:30:53.290 if I'm talking about leukemia. 00:30:53.290 --> 00:30:55.030 It may not be very compelling to a judge 00:30:55.030 --> 00:30:57.220 if I'm talking about leukemia, because it doesn't give you 00:30:57.220 --> 00:30:57.790 a number. 00:30:57.790 --> 00:30:59.830 It just tells you that there's stuff coming off, 00:30:59.830 --> 00:31:02.830 and I have an expert that says that stuff is this stuff that 00:31:02.830 --> 00:31:04.090 may cause leukemia. 00:31:04.090 --> 00:31:07.540 But it's still not 20 parts per billion of benzene, 00:31:07.540 --> 00:31:10.570 and that's ultimately what I need to show according 00:31:10.570 --> 00:31:12.520 to most judges, right. 00:31:12.520 --> 00:31:17.890 So I think, again, it's a very wide spectrum. 00:31:17.890 --> 00:31:23.710 It would vary hugely from hugely from judge to judge, 00:31:23.710 --> 00:31:26.110 and there's not any one answer. 00:31:26.110 --> 00:31:30.980 I can tell you, that as far as epidemiology goes, generally, 00:31:30.980 --> 00:31:34.240 what courts require-- if I want to show that an exposure caused 00:31:34.240 --> 00:31:36.640 my client's leukemia, for example, 00:31:36.640 --> 00:31:39.580 I would need to have a study that showed a greater 00:31:39.580 --> 00:31:42.040 than two-fold increased risk. 00:31:42.040 --> 00:31:44.110 So I have more than a doubling of the risk 00:31:44.110 --> 00:31:46.630 to a 95% confidence interval. 00:31:46.630 --> 00:31:51.790 So to eliminate the no risk and to be at least a doubling 00:31:51.790 --> 00:31:54.940 at that confidence interval, and that essentially 00:31:54.940 --> 00:31:57.220 goes to this argument that it's more probably than not 00:31:57.220 --> 00:31:59.100 caused by the exposure. 00:31:59.100 --> 00:32:01.810 Does that makes sense? 00:32:01.810 --> 00:32:03.360 OK.